Uncertainty handling in surrogate assisted optimisation of games

In this thesis entitled Uncertainty handling in surrogate assisted optimisation of games, we started out with the goal to investigate the uncertainty in game optimisation problems, as well as to identify or develop suitable optimisation algorithms. In order to approach this problem systematically, we first created a benchmark consisting of suitable game optimisation functions (GBEA). The suitability of these functions was determined using a taxonomy that was created based on the results of a literature survey of automatic game evaluation approaches. In order to improve the interpretability of the results, we also implemented an experimental framework that adds several features aiding the analysis of the results, specifically for surrogate-assisted evolutionary algorithms. After describing potentially suitable algorithms, we proposed a promising algorithm (SAPEO), to be tested on the benchmark alongside state-of-the-art optimisation algorithms. SAPEO is utilising the observation that most evolutionary algorithms only need fitness evaluations for survival selections. However, if the individuals in a population can be distinguished reliably based on predicted values, the number of function evaluations can be reduced. After a theoretical analysis of the performance limits of SAPEO, which produced very promising insights, we conducted several sets of experiments in order to answer the three central hypotheses guiding this thesis. We find that SAPEO performs comparably to state-of-the-art surrogate-assisted algorithms, but all are frequently outperformed by stand-alone evolutionary algorithms. From a more detailed analysis of the behaviour of SAPEO, we identify a few pointers that could help to further improve the performance. Before running experiments on the developed benchmark, we first verify its suitability using a second set of experiments. We find that GBEA is practical and contains interesting and challenging functions. However, we also discover that, in order to produce interpretable result with the benchmark, a set of baseline results is required. Due to this issue, we are not able to produce meaningful results with the GBEA at the time of writing. However, after more experiments are conducted with the benchmark, we will be able to interpret our results in the future. The insights developed will most likely not only be able to provide an assessment of optimisation algorithms, but can also be used to gain a deeper understanding of the characteristics of game optimisation problems

Bio-inspired computation: where we stand and what's next

In recent years, the research community has witnessed an explosion of literature dealing with the adaptation of behavioral patterns and social phenomena observed in nature towards efficiently solving complex computational tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. Notwithstanding this upsurge of activity, research in this vibrant topic should be steered towards certain areas that, despite their eventual value and impact on the field of bio-inspired computation, still remain insufficiently explored to date. The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization. An analysis and discussion are also carried out over the general trajectory followed in recent years by the community working in this field, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques

Bio-inspired computation: where we stand and what's next

In recent years, the research community has witnessed an explosion of literature dealing with the adaptation of behavioral patterns and social phenomena observed in nature towards efficiently solving complex computational tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. Notwithstanding this upsurge of activity, research in this vibrant topic should be steered towards certain areas that, despite their eventual value and impact on the field of bio-inspired computation, still remain insufficiently explored to date. The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization. An analysis and discussion are also carried out over the general trajectory followed in recent years by the community working in this field, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques

Multiobjective optimization and decision making in engineering sciences

Real-world decision making problems in various fields including engineering sciences are becoming ever more challenging to address. The consideration of various competing criteria related to, for example, business, technical, workforce, safety and environmental aspects increases the complexity of decision making and leads to problems that feature multiple competing criteria. A key challenge in such problems is the identification of the most preferred trade-off solution(s) with respect to the competing criteria. Therefore, the effective combination of data, skills, and advanced engineering and management technologies is becoming a key asset to a company urging the need to rethink how to tackle modern decision making problems. This special issue focuses on the intersection between engineering, multiple criteria decision making, multiobjective optimization, and data science. The development of new models and algorithmic methods to solve such problems is in the focus as much as the application of these concepts to real problems. This special issue was motivated by the 25th International Conference on Multiple Criteria Decision Making (MCDM2019) held in Istanbul, Turkey, in 2019.nonPeerReviewe

Evolutionary Computation

This book presents several recent advances on Evolutionary Computation, specially evolution-based optimization methods and hybrid algorithms for several applications, from optimization and learning to pattern recognition and bioinformatics. This book also presents new algorithms based on several analogies and metafores, where one of them is based on philosophy, specifically on the philosophy of praxis and dialectics. In this book it is also presented interesting applications on bioinformatics, specially the use of particle swarms to discover gene expression patterns in DNA microarrays. Therefore, this book features representative work on the field of evolutionary computation and applied sciences. The intended audience is graduate, undergraduate, researchers, and anyone who wishes to become familiar with the latest research work on this field

The Kalai-Smorodinski solution for many-objective Bayesian optimization

An ongoing aim of research in multiobjective Bayesian optimization is to extend its applicability to a large number of objectives. While coping with a limited budget of evaluations, recovering the set of optimal compromise solutions generally requires numerous observations and is less interpretable since this set tends to grow larger with the number of objectives. We thus propose to focus on a specific solution originating from game theory, the Kalai-Smorodinsky solution, which possesses attractive properties. In particular, it ensures equal marginal gains over all objectives. We further make it insensitive to a monotonic transformation of the objectives by considering the objectives in the copula space. A novel tailored algorithm is proposed to search for the solution, in the form of a Bayesian optimization algorithm: sequential sampling decisions are made based on acquisition functions that derive from an instrumental Gaussian process prior. Our approach is tested on four problems with respectively four, six, eight, and nine objectives. The method is available in the Rpackage GPGame available on CRAN at https://cran.r-project.org/package=GPGame

Global solution of constrained min-max problems with inflationary differential evolution

This paper proposes a method for the solution of constrained min-max problems. The method is tested on a benchmark of representative problems presenting different structures for the objective function and the constraints. The particular min-max problem addressed in this paper finds application in optimisation under uncertainty when the constraints need to be satisfied for all possible realisations of the uncertain quantities. Hence, the algorithm proposed in this paper search for solutions that minimise the worst possible outcome for the objective function due to the uncertainty while satisfying the constraint functions in all possible scenarios. A constraint relaxation and a scalarisation procedure are also introduced to trade-off between objective optimality and constraint satisfaction when no feasible solutions can be found