# Abstracts of (Visiting) Lectures

Seminar talk on Septemper 6th, 2017

**Santtu Tikka ** (University of Jyväskylä, Department of Mathematics and Statistics)

** Fusion: A Comprehensive Software Package for Causal Inference **

Causal inference relies on graphical models to represent causal relationships and assumptions about real-world phenomena. Fusion is a tool which enables users to effortlessly create and modify graphs through a graphical user interface, and to apply well-known inference algorithms including algorithms for identifiability, recoverability and transportability. Various tools to analyze graphs are readily available in Fusion, such as evaluation of d-separation, paths, admissible sets, instrumental variables and much more. Graphs can be easily shared and exported in publication-quality formats, and a diagnostic trace is provided for the inference algorithms.

Seminar talk on August 9th, 2017

Richard Allmendinger (Manchester Business School The University of Manchester)

** Data-driven optimization: Industrial case studies **

In this talk I will talk about several industrial case studies in data-driven optimization including (i) heuristic allocation of computational resources (joint project with ARM), (ii) online bidding for product advertisement (joint work with Dream Agility), and (iii) optimization of drug manufacturing processes (joint project with Biopharm Services). The main focus of the seminar will be on project (i), which is about deciding which computational projects (scripts) should run on which cluster such that the clusters are evenly utilized, as many projects as possible completed, and projects belonging to the same group as evenly distributed across the clusters as possible. After providing a formal problem definition, several heuristics to tackle the problem will be introduced and investigated. Finally, projects (ii) and (iii) will be introduced and computational challenges outlined, hopefully, leading to interesting discussions.

Visiting lectures on February 10th, 2017

Christer Carlsson (Abo Akademi University)

** Big Data Analytics and Knowledge Mobilisation **

The “big data” era has brought increasing demand for analytics skills as big corporations have realized that modern information and communications technology [ICT], with some interesting developments in terms of Internet of Things and Digital Economy, has brought not only blessings but also a growing set of challenges. ICT has brought the capabilities to produce large and quickly growing sets of data, which are now both growing and changing in almost real time demanding planning, problem solving and decision making also in almost real time – as this is required in digital business (“if we are late, we are out – somebody else will take the market and the customers”). The big corporations have realized that they need people with skills (i) to organize and make sense of the data, (ii) to find the logic driving events, actions and outcomes, (iii) to build models that can describe, explain and predict what is happening and (iv) to communicate the insight over a number of media to groups of people in almost real time. People with these skills have analytics expertise.

I will give an overview of a number of industrial cases that we have been working on over the years, offer some insights on how to use mathematical models to handle some fairly complex problems, explain some surprising insights we have had in some cases and show some rather unconventional (or exotic) approaches that we were surprised to find out that they actually work. One of the messages I have is the old rule of “requisite variety” – the models and the algorithms should be powerful enough to deal with the complexities offered by big data and offer platforms to actually work out essential and crucial relations - and to get the relations right - as we cannot afford to build planning, problem solving and decision making on “perceptions, impressions and experience” in a fast moving and highly competitive international business environment.

Visiting lectures on December 14th, 2016

**Ignacy Kaliszewski** (Polish Academy of Sciences)

When solving large-scale multiobjective optimization problems, solvers often get stuck with the memory or time limit. In such cases one is left with no information how far to the true Pareto front are the feasible solutions obtained till a solver stops.

In this work we show how to provide such information when solving multiobjective multidimensional knapsack problems by commercial mixed-integer linear solvers accessible on open access platforms.

We illustrate the proposed approach on bicriteria multidimensional knap sack problems derived from singleobjective multidimensional knapsack problems taken from Beasley OR Library.

Visiting lectures on November 30th, 2016

**Alberto Lovison** (Università degli Studi di Padova)

In this talk I will expose a couple of problems arising from industry and biology related to optimization naturally involving infinite dimensions.

The first is a problem coming from the arena of motorbike competitions and deals with the administration of uncertainty for the distribution of a family of trajectories. The rigorous methods available in literature lead to an analysis which is mostly qualitative.

The second problem is the attempt to explain the profile of the xylematic channels (the microtubes carrying the water from the roots to the leafs of a tree) by means of an optimization principle. Being the solution (a curve) belonging to an infinite dimensional space and given that the functional is a variable convex combination of two scalar functions, the formulation appears as an instance of multiobjective calculus of variations.

Visiting lectures on 14-27 August, 2016

**Joshua Knowles** (University of Birmingham)

ParEGO is a global optimization algorithm proposed by the presenter in 2005 for expensive multiobjective problems. Although ParEGO has been recognized by the multiobjective optimization community as an efficient method, the original implementation had several limitations. In this talk I will outline changes we have implemented in an updated release (in early 2016), and the plans for further development while keeping true to the ParEGO framework (of a scalarized version of Jones' et al's EGO method). These improvements mostly concern scalability in the decision parameters, and maximum number of evaluations possible with ParEGO, methods for incorporation of prior knowledge, and hacks for greater stability. Improvements in other aspects are also coming in forthcoming work on sParEGO (Purshouse et al), and ParASEGO* (Hakanen et al) -- very briefly outlined here. Finally, there will be a summary of the plans for a Benchmarking suite for multiobjective surrogate-assisted methods.

Visiting lectures on 7-14 August, 2016

**Handing Wang** (University of Surrey)

Most existing work on evolutionary optimization assumes that there are analytic functions for evaluating the objectives and constraints. In the real-world, however, the objective or constraint values of many optimization problems can be evaluated solely based on data and solving such optimization problems is often known as data-driven optimization. In this paper, we divide data-driven optimization problems into two categories, i.e., off-line and on-line data-driven optimization, and discuss the main challenges involved therein. An evolutionary algorithm is then presented to optimize the design of a trauma system, which is a typical off-line data-driven multi-objective optimization problem, where the objectives and constraints can be evaluated using incidents only. As each single function evaluation involves large amount of patient data, we develop a multi-fidelity surrogate management strategy to reduce the computation time of the evolutionary optimization. The main idea is to adaptively tune the approximation fidelity by clustering the original data into different numbers of clusters and a regression model is constructed to estimate the required minimum fidelity. Experimental results show that the proposed algorithm is able to save up to 90\% of computation time without much sacrifice of the solution quality.

Visiting lectures on 7-14 August, 2016

**Chaoli Sun** (University of Surrey)

Surrogate models have shown to be effective in assisting meta-heuristic algorithms for solving computationally expensive complex optimization problems. The effectiveness of existing surrogate-assisted meta-heuristics, however, has only been verified on low-dimensional optimization problems. In this paper, a surrogate-assisted cooperative swarm optimization algorithm is proposed, in which a surrogate-assisted particle swarm optimization algorithm and a surrogate-assisted social learning based particle swarm optimization algorithm cooperatively search for the global optimum. The cooperation between the particle swarm optimization and the social learning based particle swarm optimization consists in two aspects. First, they share promising solutions evaluated by the real fitness function. Second, the social learning based particle swarm optimization focuses on exploration while the particle swarm optimization concentrates on local search. Empirical studies on six 50-dimensional and six 100-dimensional benchmark problems demonstrate that the proposed algorithm is able to find high-quality solutions for high-dimensional problems on a limited computational budget.

Visiting lectures on May 30th, 2016

Robust (single-objective) optimization has been grown to an important field which is both, practically important and mathematically challenging. In contrast to this, literature on robust multi-objective optimization is rare. This may be due to the fact that the robust counterpart of an optimization problem requires the implicit determination of a worst scenario in the objective function - which in multi-objective optimization turns out to be a multi-objective optimization problem again!

This talk will first review classic and more recent robustness concepts for single-objective optimization and then present possible concepts on how robustness for a Pareto solution may be defined. In particular, we will introduce the concepts of flimsily and highly robust solutions, different variants of set-based minmax solutions and also present a generalization of the recent concept of light robustness to multi-objective problems.

Furthermore, some solution approaches on how robust efficient solutions can be computed will be shown. The concepts are illustrated on two examples: planning of short and secure flight routes and location problems.

Visiting lectures on March 16th, 2016

Sophisticated quantitative techniques for project management, notably PERT/CPM, attempt to minimize the risk that a project will fail to meet fixed requirements. But requirements themselves often vary, necessitating qualitative techniques to get or keep projects on track. This new work replaces the assumption of fixed requirements with new assumptions that allow for (1) a fully decision analytic treatment of project management decision making under uncertainty that (2) can be easily incorporated into existing project management techniques.

Visiting lectures on September 30th, 2015

**Mariano Luque** (Department of Applied Economics (Mathematics), University of Málaga)

A great variety of real decision problems involves working with multiple objective functions which are usually in conflict. Evolutionary algorithms in multiobjective optimization have proved very useful and efficient in solving many of these multiobjective optimization problems. It is therefore necessary to have appropriate evolutionary algorithms, able to solve different types of them. In this presentation I describe till three evolutionary algorithms based on the preferential information requested to the decision maker: a generating method (Global WASF-GA), one with priori information (WASF-GA) and an interactive version (Interactive WASF-GA). The three algorithms use an achievement scalarizing function (Tchebychev metric plus an augmentation term) and a set of weight vectors whose vectors formed by the inverse components are evenly distributed as possible. At each generation, all individuals are classified in different fronts taking the values of the achievement scalarizing function for the different weight vectors and for a reference point or even two for Global WASF-GA, which depend(s) on the algorithm considered. New proposals of these algorithms are also discussed.

Visiting lectures on August 11th, 2015

**Anders Forsgren** (KTH, Stockholm, Sweden)

Optimization has become an indispensable tool in many application areas. In this talk, I will discuss how research on fundamental method development and research in application areas may benefit from each other. I will discuss quasi-Newton methods and their relation to optimization problems arising in radiation therapy. In addition, I will discuss multiobjective optimization and robust optimization for radiation therapy. Finally, I will cover optimization of metabolic networks in a column-generation framework.

The research on radiation therapy is carried out jointly with RaySearch Laboratories AB. The research on cell metabolism is carried out jointly with the KTH School of Bioengineering.

Visiting lectures on June 24th, 2015

**Chaoli Sun** (University of Surrey)

Like most Evolutionary Algorithms (EAs), Particle Swarm Optimization (PSO) usually requires a large number of fitness evaluations to obtain a sufficiently good solution. This poses an obstacle for applying PSO to computationally expensive problems. We proposed two different approximation strategies on fitness evaluation, in which one utilizes fitness inheritance (called FESPSO) and the other is assisted with surrogate models (called TLSAPSO).

In FESPSO, the fitness of a particle is estimated based on its positional relationship with other particles. More precisely, once the fitness of a particle is known, either estimated or evaluated using the original objective function, the fitness of its closest neighboring particle will be estimated by the proposed estimation formula. If the fitness of its closest neighboring particle has not been evaluated using the original objective function, the minimum of all estimated fitness values on this position will be adopted. In case of more than one particle is located at the same position, the fitness of only one of them needs to be evaluated or estimated.

In TLSAPSO, a global and a number of local surrogate models are employed for fitness approximation. The global surrogate model aims to smooth out the local optima of the original multimodal fitness function and guide the swarm to fly quickly to an optimum. In the meantime, a local surrogate model constructed using the data samples near the particle is built to achieve a fitness estimation as accurate as possible.

Finally, further work on fitness approximation will be discussed focusing on integrating fitness inheritance and surrogate models in order to achieve fast convergence to the global optimum with less real fitness evaluations. In addition, data sampling is very important for a good surrogate model, especially for a global surrogate model, therefore, investigation of sampling strategies also will be our future work.

**Ran Cheng** (University of Surrey)

In evolutionary many-objective optimization, maintaining a good balance between convergence and diversity is particularly crucial to the performance of the evolutionary algorithms, which, however, becomes increasingly challenging to achieve as the number of objectives increase. To tackle this issue, a reference vector guided evolutionary algorithm is proposed. One major advantage of the proposed algorithm is that both convergence and diversity can be fully controlled by a set of predefined reference vectors, which distinguishes itself from most existing algorithms. We demonstrate that the proposed reference vector guided selection method is able to successfully address the loss of selection pressure, from which most dominance-based mechanisms suffer in dealing with many-objective optimization. Meanwhile, population diversity can also be effectively achieved with the help of reference vectors, which is of particular importance for many-objective optimization. Our experimental results on some benchmark test problems show that the proposed algorithm is highly competitive in comparison with five state-of-the-art evolutionary algorithms for many-objective optimization. In addition, we show that reference vectors are effective and cost-efficient in preference articulation, which is extremely desirable for many-objective optimization, where achieving a representative set of Pareto optimal solutions becomes intractable using a limited population size. Furthermore, a reference vector regeneration strategy is proposed for handling irregular Pareto fronts. Finally, the proposed algorithm is extended for solving constrained many-objective optimization problems.

Visiting lectures on April 7th, 2015

**Francisco Ruiz**(University of Málaga, Spain)

Interactive methods have proved to be extremely useful multiobjective techniques, when it comes to solve real complex decision making problems. Their iterative schemes are especially suitable for the necessary learning process that has to be present in every decision making process. Many different interactive methods exist, and they vary both in the type of information that the decision maker (DM) has to provide at each iteration, and in the way the different solutions are obtained along the process. The information required from the DM can take many different forms (just choosing one solution among a set of possible solutions, giving local tradeoffs, giving reference or target values, classifying the objectives…). But in many cases, the interactive method is chosen without taking into account the cognitive burden that it implies for the DM. In this sense, we have developed hybrid interactive multiobjective systems, where the DM can decide at each step the type of information (s)he prefers to give, and the system internally switches to the most appropriated method. The idea is to adapt the resolution process to the necessities of the DM, and not vice versa. We have applied these interactive systems to several real problems, including the budget assignment to the hospitals of our Regional Sanitary System, the determination of the optimal electricity mix of Andalucía, or the calculation of the optimal dimensions of a solar thermal plant.

**Tomas Back**(Natural Computing Group, Leiden Institute of Advanced Computer Science (LIACS), Leiden University, Netherlands)

Industrial optimization problems often characterized by a number of challenging properties, such as time-consuming function evaluations, high dimensionality, a large number of constraints, and multiple optimization objectives.

Working with Evolutionary Strategies, we have optimized them over the past decades for such optimization problems. Certain variants are particularly effective, and set-oriented selection criteria such as SMS-EMOA are useful for approximating the Pareto front in case of multiobjective optimization.

In this presentation, we will illustrate these aspects by referring to industrial optimization problems, such as they occur in the automotive and many other industries. We will show that evolutionary strategies can be very effective even in case of very small numbers of functions evaluations, and that they can approximate Pareto fronts very well.

Visiting lecture on March 11th, 2015

**Kerstin Daechert** (University of Duisburg-Essen, Germany)

In this talk we present an algorithm that computes the nondominated set or a subset of it by solving a sequence of scalarizations whose parameters are varied in an adaptive way. More precisely, the parameters are chosen so that with every scalarization solved either a new nondominated point is computed or the investigated part of the search region, i.e. the part of the outcome space containing possibly further nondominated points, can be discarded. Besides an appropriate computation of the parameters, the main ingredient of such an adaptive parametric algorithm is a systematic decomposition of the search region.

In the first part of our talk, we present a redundancy-free decomposition which permits to show that the number of scalarized optimization problems that need to be solved in order to generate the entire nondominated set (finiteness assumed) depends linearly on the number of nondominated points in the tricriteria case. This improves former results which showed a quadratic dependence in the worst case.

The presented adaptive parametric algorithm is not restricted to a special scalarization and can be used, e.g., with the classic e-constraint or the weighted Tchebycheff method. For the augmented variants of these scalarizations, particularly for the augmented weighted Tchebycheff method, we show in the second part of our talk how all parameters, including the one associated to the augmentation term, can be chosen adaptively such that a nondominated point in a selected part of the search region is generated whenever there exists one and, at the same time, the augmentation parameter is chosen as large as possible in order to avoid numerical difficulties reported in the literature.

In the third and concluding part, we validate our theoretical findings for the tricriteria case by numerical tests. Moreover, we demonstrate the flexibility of the presented algorithm by applying it also to continuous multicriteria optimization problems. Finally, we discuss how to generalize the algorithm to any number of criteria.

Visiting lecture on February 9th, 2015

A problem of visualizing the optimal set of solutions of a multi-objective optimization problem is considered. We focus on discovery the structure of multidimensional decisions space by visualization of Pareto sets as well as on the relationship between objective functions and design variables in optimal set of solutions. A multi-objective visualization method is proposed to visualize a set of efficient points, e.g. the multidimensional points, as points in the two-dimensional space.

Visiting lecture on November 13th, 2014

**Montaz Ali** (University of Witwatersrand, Johannesburg, South Africa)

It goes without saying that the solutions to system of linear equations are of paramount importance in almost every field of science, engineering and management. We will consider the following linear system Ax = b where A ϵ R^(m×n), x ϵ R^n, b ϵ R^m. In practice, it is common to encounter systems that do not admit a unique solution, rather the system is consistent where the number of variables exceeds the number of equations - in which case an infinite number of solutions exist, or the system is inconsistent and no solutions exist. In the former case the system is called underdetermined and in the latter the system is called over-determined. When the system is underdetermined we have in general m <n, and practitioners seek to pick the solution that is best suited to their needs. Minimum infinity norm solutions are often chosen for various practical problems.

The problem is to minimize the maximum value of elements in the solution vector. Such a solution is chosen when one seeks to minimize the maximum load on any node of a given system. In particular, such solutions are sought in control theory when the limitations of any individual component of a system cannot be breached.

The current best algorithms for the solution to the problem are given in [1, 2, 3, 4]. These are the path following algorithm was proposed by Cadzow [1] in which the polyhedral structure of the objective function of the dual was Exploited. Abdelmalek [2] some years later proposed a linear programming formulation of the dual problem in which a modified simplex method was applied to a reduced tableu - this being made possible by the strong symmetries present in the constraint matrix. Shim and Yoon [3] almost two decades later proposed a primal method which they claim is conceptually and geometrically clear at the cost of computational inferiority to both of the methods already mentioned.

We propose a primal path following method to deal with the problem. Unlike the dual approach suggested in [1], our method is based on the primal formulation and yet conceptually different from the primal method in [3]. In particular, our method employs novel heuristics coupled with ideas that have been exploited for the over-determined system. Results and comparisons with the existing method will also be shown. An iteration complexity analysis will also be presented.

### References

- James A. Cadzow. An efficient algorithmic procedure for obtaining a minimum L-infinity norm solution to a system of consistent linear equations. SIAM Journal on Numerical Analysis, 11(6): pp. 1151--1165, 1974.
- Nabih N Abdelmalek. Minimum l-infinity solution of underdetermined systems of linear equations. Journal of Approximation Theory, 20(1): pp. 57--69, 1977.
- Ick-Chan Shim and Yong-San Yoon. Stabilized minimum infinity norm torque solution for redundant manipulators. Robotica, 16(2):pp. 193--205, 1998.
- Insso Ha and Jihong Lee. Analysis on a minimum infinity norm solution for kinematically redundant manipulators. ICASE, 4(2): pp. 130--139, 2002.

Visiting lecture on August 7th, 2014

**Kalyanmoy Deb** (Koenig Endowed Chair Professor, Michigan State University, East Lansing, USA)

Many practical problem‐solving tasks involve multiple hierarchical search processes, requiring one search and optimization task to solve another lower--‐level search and Optimization problem in a nested manner. In their simplicity, these problems are known as "Bilevel optimization" problems, in which there are two levels of optimization tasks. Problems in economics and business involving company CEOs and department heads or governmental decision makers and NGOs are standard bilevel optimization problems. In engineering, optimal control problems, structural design problems, transportation problems and other hierarchical problems fall into this category. These problems are also known as Stackelberg games in the operations research and computer science community. Due to nestedness and inherent complexities involved in solving bilevel problems, evolutionary methods are increasingly being found to be effective. In this talk, we shall discuss some of the key developments of evolutionary bilevel optimization (EBO) and highlight some promising areas of research in this area.

Visiting lecture on June 12th, 2014

**Ralph L. Keeney** (Duke University, USA)

This seminar will discuss the art and science identifying and structuring values for any decision situation. It will present practical techniques to initially create a useful set of values. It will also include a workshop element where participants will practice identifying objectives for a relevant practical application. Then the concepts necessary to structure a set of values are presented. The seminar will indicate the essential role that values have for making good decisions.

Visiting lecture on May 21st, 2014

The General Algebraic Modeling System (GAMS) is a high-level modeling system for mathematical programming and optimization. We will start off by covering the basic concepts and the design principles of the GAMS system. Then we will highlight some good modeling practices, as well as, look at a simple example. Finally, we will consider a large scale energy model application.

Visiting lecture on August 23, 2013:

**Kalyanmoy Deb** (Michigan State University)

Evolutionary Many-Objective Optimization

Evolutionary optimization methods were found to be apt in solving two and three-objective optimization problems, particularly in aiding to find a representative set of trade-off points before making a decision for a preferred solution. Efforts were underway for the past decade in trying to come up with an efficient methodology for handling many-objective optimization problems involving more than three objectives. In the recently proposed NSGA-III approach, the task of diversity preservation is assisted by specifying some lead reference points and the task of convergence near to Pareto-optimal front is achieved by algorithmic means. Results on problems having up to 15 objectives will be shown.

Visiting lecture on August 12, 2013:

**Gilberto Montibeller** (London School of Economics)

Developing Risk Management Support Systems for the Prioritization of Emerging Health Threats

The prioritisation and management of emerging threats to human and animal health pose serious challenges for policy makers. Such challenges have many sources. First, the emerging nature of such threats, coupled with limited impact modelling, means that often there is lack of reliable evidence about impacts and probability of an outbreak. Second, the continuing emergence of multiple threats, often contemporary, requires regular prioritization informed by the amalgamation of different sources of quantitative and qualitative data, and experts' judgments. Third, policy makers are often concerned with multiple impacts that go beyond health and economic concerns, including issues related with public perception and capability building. In this paper we suggest how decision analysis could address these challenges both from an analytical and, more critically, organizational perspective. In particular we argue that the development and use of simple and tailored risk management systems, when appropriately embedded into organizational routines, can provide effective support for the assessment of emerging threats and the design of policy recommendations. We illustrate our suggestions with a real- world case study, in which we developed a risk management support system for DEFRA, the UK Government Department for Environment, Food and Rural Affairs, to help with their prioritization of emerging threats to the countrys animal health status.

Visiting lecture on April 24, 2013:

Solving single- and multi-objective evolutionary design optimization problems in structural and civil engineering

Several optimum design problems concerning the field of structural and civil engineering are solved using numerical methods and evolutionary algorithms. Concretely, the description of methodology and final optimum solutions of different single- and multi-objective applications are shown related to: structural engineering optimum design (constrained weight and reliability), noise barrier optimum design (insertion loss and barrier height), and slope stability optimum design (factor of safety and slope height).

Visiting lecture on August 16th, 2012:

**Yaochu Jin** (University of Surrey, UK)

A Systems Approach to Evolutionary Optimization of Complex Engineering Problems

Real-world complex engineering optimisation remains a challenging issue in evolutionary optimisation. This talk discusses the major challenges we face in applying evolutionary algorithms (EAs) to complex engineering optimization, including representation, the involvement of time-consuming and multi-disciplinary quality evaluation processes, changing environments, vagueness in formulating criteria formulation, and the involvement of multiple sub-systems. We propose that the successful tackling of all these aspects give birth to a systems approach to evolutionary design optimization characterized by considerations at four levels, namely, the system property level, temporal level, spatial level and process level. Finally, we suggest a few promising future research topics in evolutionary optimisation that consist in the necessary steps towards a life-like design approach, where design principles found in biological systems such as self-organization, self-repair and scalability play a central role.

Visiting lecture on October 31st, 2012:

**Jouni Lampinen** (Vaasan yliopisto)

Globaali optimointi datan luokittelussa

Vaasan yliopiston ja Lappeenrannan teknillisen yliopiston yhteisessä tutkimushankkeessa kehitetään uuden tyyppistä globaalia yksi- ja monitavoiteoptimointia hyödyntävää datan luokittelumenetelmää. Keskeisenä kontruktiivisena tavoitteena on, että kehitettävä järjestelmä sopeutuisi automaattisesti luokiteltavan datan ominaisuuksiin mm. valitsemalla automaattisesti luokiteltavan datan ominaisuuksiin parhaiten sopivan luokitinmallin ja optimoi kaikki valittuun malliin liittyvät vapaat parametrit.

Järjestelmän keskeiset osat ovat luokitinmalli ja sen optimointiin käytettävä algoritmi. Käytetty luokitinmalli pohjautuu ns. lähimmän prototyyppivektorin menetelmään. Mallin sovittaminen luokiteltavaan dataan suoritetaan globaalin optimoinnin keinoin. Optimointialgoritmina käytetään evoluutiolaskennan menetelmiin kuuluvaa differentiaalievoluutioalgoritmia.

Tähän mennessä on luokitinmallin optimointi on suoritettu yksitavoiteoptimointina, jossa optimoinnin tavoite on ollut virheellisesti luokiteltujen datapisteiden määrän minimointi. Jatkossa monitavoiteoptimoinnin soveltamisen odotetaan mahdollistavan myös useamman luokittelutarkkuuden ja -laadun kriteerin samanaikaisen huomioimisen.

Hankkeen tavoitteena on kehittää uudentyyppinen luokittelumenetelmä, jossa evoluutiolaskennan ja globaalin optimoinnin avulla pyritään kehittämään tarkkuudeltaan aiempia tarkempi, monipuolisempi ja helppokäyttöisempi datan luokittelumenetelmä.

Visiting lecture on August 16th, 2012:

**Yaochu Jin** (University of Surrey, UK)

A Systems Approach to Evolutionary Optimization of Complex Engineering Problems

Real-world complex engineering optimisation remains a challenging issue in evolutionary optimisation. This talk discusses the major challenges we face in applying evolutionary algorithms (EAs) to complex engineering optimization, including representation, the involvement of time-consuming and multi-disciplinary quality evaluation processes, changing environments, vagueness in formulating criteria formulation, and the involvement of multiple sub-systems. We propose that the successful tackling of all these aspects give birth to a systems approach to evolutionary design optimization characterized by considerations at four levels, namely, the system property level, temporal level, spatial level and process level. Finally, we suggest a few promising future research topics in evolutionary optimisation that consist in the necessary steps towards a life-like design approach, where design principles found in biological systems such as self-organization, self-repair and scalability play a central role.

Visiting lecture on May 21st, 2012:

**Carlos Coello Coello** (CINVESTAV-IPN, Mexico)

Recent Results and Open Problems in Evolutionary Multiobjective Optimization

Evolutionary algorithms (as well as a number of other metaheuristics) have become a popular choice for solving problems having two or more (often conflicting) objectives (the so-called multi-objective optimization problems). This area, known as EMOO (Evolutionary Multi-Objective Optimization) has had an important growth in the last 15 years, and several people (particularly newcomers) get the impression that it is now very difficult to make contributions of sufficient value to justify, for example, a PhD thesis. However, a lot of interesting research is still under way. In this talk, we will review some of the research topics on evolutionary multi-objective optimization that are currently attracting a lot of interest (e.g., handling many objectives, hybridization, indicator-based selection, use of surrogates, etc.) and which represent good opportunities for doing research. Some of the challenges currently faced by this discipline will also be delineated.

Visiting lecture on September 1st, 2011:

**Murat Köksalan** (Middle-East Technical University, Ankara, Turkey)

Solving Multiobjective Mixed Integer Programs

We develop an algorithm to find the best solution for multiobjective integer programs when the DM's preferences are consistent with a quasiconcave utility function. Based on the convex cones derived from past preferences, we characterize the solution space that excludes inferior regions. We guarantee finding the most preferred solution and our computational results show that the algorithm works effectively.

Visiting lecture on August 17th, 2011:

**Anders Forsgren** (Optimization and Systems Theory, KTH, Sweden)

Optimization of Radiation Therapy

Optimization has become an indispensable tool for radiation therapy. In this talk, we highlight fundamental aspects of the optimization problems that arise, and also discuss more advanced aspects, such as how to handle conflicting treatment goals and model uncertainty.

We initially discuss how problem structure may be taken into account for computing approximate solutions to the fundamental optimization problem that arises in radiation therapy. We then show how conflicting treatment goals may be handled in a multiobjective formulation by approximation of the Pareto surface. Finally, we discuss how uncertainties in range, setup and organ motion may be handled in a robust optimization framework for optimization of proton therapy.

The talk is based on joint research between KTH and RaySearch Laboratories AB, in particular research carried out by Rasmus Bokrantz, Albin Fredriksson and Fredrik Lofman.

Visiting lecture on August 9th, 2011:

**João Pedro Pedroso** (University of Porto, Portugal)

Issues on algorithm self-tuning

In this talk I will raise some topics arising in algorithm parameterization, and on their view as a problem of noisy, non-linear optimization. I will present data obtained with the parameterization of some metaheuristics for combinatorial optimization, and propose a method for automatically tuning parameters. The main objective is to open discussion, and to initiate a brainstorming session on what could be done for advancing the state-of-the-art on this issue.

Visiting lecture on June 7th, 2011:

Battery Thermal Packaging Design

The performance of batteries is critical for the mobility and performance of automotive vehicles. In order to maintain battery life and performance, it is crucial to keep the batteries within the temperature range in which their operating characteristics are optimal. To achieve the desired tension and current required for different applications, the cells are packed together in modules which in turn are connected in parallel or in series. To provide a reliable battery, the temperature of the pack should be kept inside the temperature range. The uniformity of the temperature inside the pack depends mainly on the non-uniform heat transfer efficiency among the cells. Due to the capacity unbalance, some cells may experience over/under charging which leads to premature battery failure.

The cell optimal layout inside the battery pack is optimized while considering thermal aspects. Due to a large number of function evaluations, computational fluid dynamics models are not suitable and a simplified lumped parameter thermal model is integrated with the optimization. The optimization problem is formulated as a constrained multiobjective program in which objective functions (to be minimized) represent descrepancies between the operating cell temperatures and the target temperature. Such a formulation with physically homogeneous and comparable objectives is addressed in terms of the equitability preference rather than the traditional Pareto preference. Mathematical aspects of the equitability preference are investigated and the applicability of the equitable solutions to engineering problems is discussed. Furthermore, the battery location in the vehicle is also optimized to improve vehicle dynamics, component accessibility and passenger survivability while considering geometric constraints including collision and overlap among the components. The overall optimization problem is two-level. A solution method to the overall problem is outlined.

Visiting lecture on November 30th, 2010:

**Antanas Zilinskas** (Vilnius University, Institute of Mathematics and Informatics, Vilnius, Lithuania)

P-algorithm for black box multiobjective optimization

Statistical models of multimodal objective functions have been successfully applied for the construction of black box single objective global optimization algorithms. P-algorithm is based on a statistical model and is defined as the repeated decision making under uncertainty. The axioms of rationality are formulated taking into account the situation of selecting a point for the current computation of the objective function value. The formulated axioms imply the selection of the point of maximum probability to improve the best known value of the objective function. In the present talk the P-algorithm is generalized to multiobjective optimization. An example illustrating properties of the newly proposed algorithm is included.

Visiting lecture on September 16th, 2010:

**Theodor Stewart** (University of Cape Town, South Africa and Manchester Business School, UK)

Simon French & Jesus Rios
(Manchester Business School)

Scenario Planning and Multiple Criteria Decision Analysis

Scenario planning in its various forms is a widely used approach to strategic planning. It provides a mechanism for sharing understanding of major sources of risk and uncertainty in decision making. In many instances, however, scenario planning does not make use of formal analytical tools for evaluation of potential courses of action. The field of multiple criteria decision analysis (MCDA), on the other hand, has developed powerful tools and algorithms for the evaluation and choice of alternative strategies in the presence of multiple and conflicting objectives. Many approaches to MCDA, however, do not employ formal methods for dealing with substantial uncertainties in outcomes. We thus discuss some approaches to integration of scenario planning and multiple criteria decision analysis, to capture the power of both. The concepts are applicable to any of the broad schools of MCDA, as well as to both discrete choice and continuous problems.

Visiting lecture on August 25th, 2010:

**K. Kufer** (Univ. Kaiserslautern, Germany)

Interactive decision support - multicriteria optimization in practice

Optimization problems in class room go out from a well defined problem setting: the set of feasible solutions is given as well as the objective function(s). When it comes to practice we often find an incomplete description of what is feasible and what not. Even more complicated is it to distinguish between good and bad or even to characterize optimality.

The talk emphasizes this major problem of not rigorously given problems and shows how multicriteria optimization and interactive decision support by visualization of solutions might help in this dilemma. Problems and methods are discussed in the context of industrial problems Fraunhofer ITWM is currently involved in.

Visiting lecture on May 24th, 2010:

Stochastic Programming for Business and Technology

The concept of implementable nonlinear stochastic programming by finite series of Monte-Carlo samples is surveyed addressing to topics related with stochastic differentiation, stopping rules, conditions of convergence, rational setting of parameters of algorithms, etc. Our approach distinguishes by treatment of the accuracy of the solution in a statistical manner, testing the hypothesis of optimality according to statistical criteria and estimating confidence intervals of the objective and constraint functions. The rule for adjusting the Monte-Carlo sample size is introduced which ensures the convergence by linear rate and enables us to solve the stochastic optimization problem using a reasonable number of Monte-Carlo trials. Issues of implementation of the developed approach in financial management, business management and engineering are considered, too.

Visiting lecture on February 10th, 2010:

**Ralph E. Steuer **(University of Georgia,
USA)

An Overview in Graphs of Portfolio-Selection Efficient Frontiers and Surfaces in Finance

Being able to render an efficient frontier quickly is an important attribute of the systems used to support decision making in portfolio selection. In standard portfolio selection, simplifying assumptions generally make this possible. But in the larger problems that are beginning to appear with greater frequency, the assumptions can cause more trouble than they are worth. This is shown along with their computational implications. Also shown are the effects on standard portfolio selection of inserting additional criteria (such as dividends, liquidity, etc.) into the portfolio selection process.

One is that this causes the efficient frontier to turn into an efficient surface. Another is that the efficient surface has a tendency to be formed by a collection of platelets (like on the back of a turtle). A third concerns the availability of algorithms capable of computing the platelets. And a fourth is how to search a surface of platelets for one's most preferred portfolio on it. Computational results are reported where possible to support the graphs presented.

Visiting lecture on January 18th, 2010:

**Matthias Ehrgott** (The University of Auckland, New
Zealand)

1. An approximation algorithm for convex multi-objective programming problems

In multi-objective optimization, several objective functions have to be minimized simultaneously. We propose a method for approximating the nondominated set of a multi-objective nonlinear programming problem, where the objective functions are convex and the feasible set is convex. This method is an extension of Benson's outer approximation algorithm for multi-objective linear programming problems. We prove that this method provides a set of epsilon-nondominated points. For the case that the objectives and constraints are differentiable, we describe an efficient way to carry out the main step of the algorithm, the construction of a hyperplane seperating an exterior point from the feasible set in objective space. We provide examples that show that this cannot always be done in the same way in the case of non-differentiable objectives or constraints.

2. Finite representation of nondominated sets in multiobjective linear programming

In this paper we address the problem of representing the continuous set of nondominated solutions of a multiobjective linear programme by a finite subset of such points. We prove that a related decision problem is NP-hard. Moreover we illustrate the drawbacks of the known global hooting, normal boundary intersection and normal constraint methods concerning the coverage of the nondominated set and uniformity of the representation by examples. We propose a method which combines the global shooting and normal boundary intersection methods. By doing so, we overcome the limitations of these methods. We show that our method computes a set of evenly distributed nondominated points for which the the coverage error and the uniformity level can be measured. Finally, we apply this method to an optimization problem in radiation therapy and present illustrative results for some clinical cases.

Visiting lecture on December 15th, 2009:

**Michael Monz** (Fraunhofer-ITWM, Germany)

Interactive Planning of Intensity Modulated Radiation Therapy (IMRT)

IMRT can be used for curative treatment even if the tumor is of complicated shape or close to important risk organs. Since the degrees of freedom vastly exceed the number one can manually handle, the planning is done using so called inverse planning. Here, each considered plan is the result of a large-scale optimization problem.

The planning problem naturally is a multi-criteria problem: To each relevant organ at risk a function is assigned. Furthermore, the different tumor volumes each get one to two functions. The planning process now tries to find a compromise between the specified goals. Currently, this most often done by iterative changes to the model or iterative changes to the weights with which the different functions contribute to the overall objective. We will call this tedious and time consuming procedure human iteration loop (HIL).

Explicitly treating the problem as a multi-criteria problem offers the possibility to greatly improve the planning process. Yet the problem under consideration does not make it easily accessible for multi-criteria optimization: There is no generally acknowledged model for the quality of a plan, the problem is large scale and gets almost intractable, if all degrees of freedom are included into the optimization.

The talk will introduce IMRT planning and describe how the described problems were tackled and what open questions are still actively being researched.

Visiting lecture on December 10th, 2009:

**Timo Laukkanen** (Helsinki University of
Technology)

Modelling, simulation and optimization of energy systems at ENY

The modelling, simulation and optimization research in the Energy Engineering an Environmental Protection (ENY)-research group has aimed at developing systematic tools and models to analyze and optimize energy systems so that they are cost- and energy efficient. Focus is mainly on single and multiobjective models that belong to the class of deterministic Mixed Integer NonLinear Programming (MINLP) problems, where the general problem is to cope with both combinatorial issues due to integer (binary) variables and with non-convexities due to nonlinearity. Modelling and simulation of energy systems with commercial simulation software has also been an important activity especially regarding case-specific industrial applications. In this presentation, different methods, models and applications of energy systems currently or previously researched at ENY are presented. Also the main commercial simulation software used and GAMS (General Algebraic Modelling System), which is and has been the main modelling platform, are shortly presented. Finally the future possibilities are shortly discussed.

Visiting lecture on November 10th, 2009:

**Georges Fadel** (Clemson University, USA)

Optimization and complex systems design - the packaging / layout problem

The presentation will describe some mechanical design problems that are considered complex, namely packaging (compact packing) and layout or configuration design. Complexity in this context will be defined highlighting the multiple interactions that need to be considered and the geometry and other characteristics that contribute to that complexity. Next, the past work on developing an approach to deal with that complexity will be shown as it evolved over the years leading to its current state. The presentation will describe the development of an archive based micro genetic algorithm as well as various geometrical considerations that had to be efficiently managed to solve the problem. The talk will conclude with some of the extensions of that work, describing the design of heterogeneous components.

Lecture on May 25th, 2009:

**Timo Aittokoski** (University of Jyväskylä,
Finland)

Efficient Evolutionary Method to Approximate the Pareto Optimal Set in Multiobjective Optimization

Solving real-life engineering problems requires often multiobjective, global and efficient (in terms of objective function evaluations) treatment. In this study, we consider problems of this type by discussing some drawbacks of the current methods and then introduce a new population based multiobjective optimization algorithm which produces a dense (not limited to the population size) approximation of the Pareto optimal set in a computationally effective manner.

Visiting lecture on December 9th, 2008:

**Ausra Mackute** (Institute of
Mathematics and Informatics, Vilnius, Lithuania)

On few applications of multi-criteria optimization

Applied optimization problems such as process design or optimal control are multi - criteria problems in essence. It is important to construct feasible solution set, but in case these problems are combined with the use of nonlinear models, generation of reliable Pareto front can be difficult. A case study in process design is used to illustrate the multi-step procedure for generating Pareto front for a two criteria problem. The base of this procedure is high-dimensional data analysis and visualization techniques. The results show that the use of data analysis and visualization can help gain insight into the Pareto optimal. Another case study in practical optimal control problem from biotechnology is used for comparison of several multi-criteria optimization methods . Two criteria are taken into account: yield of biomass and natural process duration. Theoretical analysis of the problem is difficult because of non-linearity of the process’ model. The problem is reduced to a parametric optimization problem by means of parameterization of control functions. Several evolutionary multi-criteria optimization algorithms and a scalarization based direct search algorithms are considered. The methods are compared with respect to the precision and the solution time.

Visiting lectures on November 17th, 2008:

**Ankur Sinha** (Helsinki School of Economics/IIT
Kanpur, India)

Solving Bilevel Multi-Objective Optimization Problems Using Evolutionary Algorithms

Bilevel optimization problems require every feasible upper-level solution to satisfy optimality of a lower-level optimization problem. These problems commonly appear in many practical problem solving tasks including optimal control, process optimization, game-playing strategy development, transportation problems, and others. In the context of a bilevel single objective problem, there exists a number of theoretical, numerical, and evolutionary optimization results. However, there does not exist too many studies in the context of having multiple objectives in each level of a bilevel optimization problem. I shall address bilevel multi-objective optimization issues and discuss a viable algorithm based on evolutionary multi-objective optimization (EMO) principles to handle such problems.

**Shyam Prasad Kodali** (Helsinki School of
Economics/IIT Kanpur, India)

Application of Genetic Algorithms to Tomographic Reconstruction

Tomographic reconstruction is an inverse problem wherein, we reconstruct the image of an object using what are called projection data. The aim is to be able to visualize the inside of an object in a non-invasive manner. Tomography is very popular in the field of medical science and is gaining popularity in other areas including non destructive evaluation, and geosciences. The projection data collected depends on the tomographic principle used to acquire the data. For example in ultrasonic tomography this could be the time-of-flight of an ultrasonic wave through a specimen or the attenuation an ultrasound wave undergoes when it passes through the specimen. In this lecture, we briefly present the tomographic principles and the different methods in use for solving the reconstruction problem. We further present some preliminary results of application of a GA-based algorithm to tomographic reconstruction with and without noise.

**Jussi Hakanen** (University of Jyväskylä,
Finland)

Simulation-based Interactive Multiobjective Optimization in Wastewater Treatment

This paper deals with developing tools for wastewater treatment plant design. The idea is to utilize interactive multiobjective optimization which enables the designer to consider the design with respect to several conflicting evaluation criteria simultaneously. This is especially important because the requirements for wastewater treatment plants are getting more and more strict. By combining a process simulator to simulate wastewater treatment and an interactive multiobjective optimization software to aid the designer during the design process, we obtain a practically useful tool for decision support. The applicability of our methodology is illustrated through a case study related to municipal wastewater treatment where three conflicting evaluation criteria are considered.

**Sauli Ruuska** (University of Jyväskylä,
Finland)

The Effect of Trial Point Generation Schemes on the Efficiency of Population-Based Global Optimization Algorithms

Many practical optimization problems in engineering are solved using population-based direct search methods because of their wide applicability and ease of use. The purpose of this paper is to bring attention to the deficiences of the point generation schemes in some of the currently used population-based algorithms. We focus on the population-based algorithms such as Controlled Random Search and Differential Evolution which use the population also as the source of perturbations used to generate new trial points. The situations in which these algorithms generate a large number of unsuccesful trial points or fail to generate succesful trial points altogether are illustrated.

Visiting lecture on November 12th, 2008:

**Francisco Ruiz** (University of
Malaga, Spain)

A Multiobjective Interactive Approach to Determine the Optimal Electricity Mix of Andalucia

The principles of sustainability imply the joint consideration of economical, social and environmental criteria in every decisional process. Electricity is, of course, a basic need of our modern society, but the production processes in the Spanish region of Andalucia have traditionally been aggressive for the environment, due to the high number of plants using fossil fuels (mainly coal and petrol) some decades ago, and combined cycle plants that work with natural gas (more recently). On the other hand, the alternative renewable sources (eolic, solar, hydraulic...), being more respectful for the environment, are usually much more expensive. In such a framework, multiple criteria decision making techniques can be extremely helpful.

This talk reports the use of interactive multiobjective methods to determine the most adequate electrical mix for Andalucia. This study was financed by the Regional Ministry of Environment, and we have considered eight different electricity generation techniques (comprising non renewable and renewable sources). As for the criteria, we have used the yearly costs and the vulnerability (dependence on imported fuels) as economical-strategic criteria, and the environmental issues have been addressed through the consideration of twelve impact categories which have in turn been assessed using a life cycle analysis scheme. The problem is to decide the percentage of the electricity to be produced by each of the eight generation techniques.

This problem has been solved using the interactive multiobjective programming package PROMOIN. This package allows the simultaneous use of different interactive multiobjective techniques, in such a way the the Decision Maker (DM) can change the type of information he wishes to provide at each iteration (local tradeoffs or weights, reference points, choosing a solution among several ones...), and the interactive procedures is changed accordingly. This provides a flexible resolution framework, which was highly appreciated by the DMs. This talk reports both the modeling of the problem, and the resolution process that was carried out.

Visiting lecture on October 28th, 2008:

**Andrzej P. Wierzbicki** (National
Institute of Telecommunications, Poland)

1. Delays in Technology Development: Their Impact on the Issues of Determinism, Autonomy and Controllability of Technology

The paper provides a discussion of diverse delays occurring in the development and utilization of technology products, and an explanation of reasons why, when seen holistically from outside, the process of technology development might appear as an autonomous, self-determining, uncontrollable process. When seen from inside, however, e.g., from the perspective of software development and evaluation, the process is far from being uncontrollable. This paradox is explained by the fact that technology development contains many processes with delays, in total amounting sometimes to fifty years; when seen from outside, such a process might appear uncontrollable, even if it is very much controllable when approached internally and in detail. Therefore, the definition and some types of technology creation as well as some stages of technological processes are discussed in some detail in this paper. Some aspects of the contemporary informational revolution and some recent results on micro-theories of knowledge and technology creation are also reviewed. It is suggested that one of possible ways of changing the paradigmatic attitude of philosophy of technology is to invite some such philosophers to participate in the development of modern tools of knowledge civilization era: software development and evaluation; moreover, inputs from philosophy of technology might enrich such processes. On the other hand, without participating in software development and evaluation, philosophy of technology runs the risk of becoming outdated and sterile. The conclusions of the paper stress the need of essentially new approaches to many issues, such as software development and evaluation versus philosophy of technology, in the time when informational revolution results in a transition towards knowledge civilization.

2. Ontology Construction and Its Applications in Local Research Communities

Ontological engineering has been widely used for diverse purposes in different communities and a number of approaches have been reported for developing ontologies; however, few works address issues of specific ontology construction for local communities, especially when taking into account the specificity of academic knowledge creation. This Chapter summarizes efforts done in two cooperating communities in Japan and in Poland, including attempts to clarify the concept and the field of knowledge science, to create an ontology characterizing a research program in this field, then to apply related results in another field – contemporary telecommunications. The distinctive approach to ontology creation (see Ren at al. 2008) is based on a combination of bottom-up and top-down approaches with the purpose of combining explicit knowledge with tacit, intuitive and experiential knowledge for constructing an ontology. Other possible views on constructing ontology are also presented and discussed; lessons from an ongoing application of this approach to a local research community working on contemporary telecommunication issues in Poland are also discussed. The combination of explicit and tacit, intuitive and experiential knowledge has led to a development of a software system named adaptive hermeneutic agent (AHA), a toolkit for documents gathering, keywords extracting, keywords clustering, and ontology visualization.

Visiting lecture on September 25th, 2008:

**Leoneed Kirilov** (Bulgarian Academy of Sciences,
Sofia, Bulgary)

The problem of Metal Buiding-up by Welding and Multiple Criteria Decision Support

The problem for process optimizing of metal building-up by welding is investigated. A multiple criteria model with four objectives is suggested. The interactive satisfying trade-off method of Nakayama is used to solve the model.

Visiting lecture on May 9th, 2008:

**Eckart Zitzler, Johannes Bader** (ETH, Zurich,
Switzerland)

Approximating the Pareto Set Using Set Preference Relations: A New Perspective On Evolutionary Multiobjective Optimization

Assuming that evolutionary multiobjective optimization (EMO) mainly deals with set problems, one can identify three core questions in this area of research: (i) how to formalize what type of Pareto set approximation is sought, (ii) how to use this information within an algorithm to efficiently search for a good Pareto set approximation, and (iii) how to compare the Pareto set approximations generated by different optimizers with respect to the formalized optimization goal. There is a vast amount of studies addressing these issues from different angles, but so far only few studies can be found that consider all questions under one roof.

This talk is an attempt to summarize recent developments in the EMO field within a unifying theory of set-based multiobjective search. It discusses how preference relations on sets can be formally defined, gives examples for selected user preferences, and proposes a general, preference-independent hill climber for multiobjective optimization with theoretical convergence properties. The proposed methodology brings together preference articulation, algorithm design, and performance assessment under one framework and thereby opens up a new perspective on EMO.