The Automated Design of Probabilistic Selection Methods for Evolutionary Algorithms

Selection functions enable Evolutionary Algorithms (EAs) to apply selection pressure to a population of individuals, by regulating the probability that an individual\u27s genes survive, typically based on fitness. Various conventional fitness based selection methods exist, each providing a unique relationship between the fitnesses of individuals in a population and their chances of selection. However, the full space of selection algorithms is only limited by max algorithm size, and each possible selection algorithm is optimal for some EA configuration applied to a particular problem class. Therefore, improved performance may be expected by tuning an EA\u27s selection algorithm to the problem at hand, rather than employing a conventional selection method. The objective of this paper is to investigate the extent to which performance can be improved by tuning selection algorithms, employing a Hyper-heuristic to explore the space of search algorithms which encode the relationships between the fitnesses of individuals and their probability of selection. We show the improved performance obtained versus conventional selection functions on fixed instances from a benchmark problem class, including separate testing instances to show generalization of the improved performance

Evolved parameterized selection for evolutionary algorithms

Selection functions enable Evolutionary Algorithms (EAs) to apply selection pressure to a population of individuals, by regulating the probability that an individual\u27s genes survive, typically based on fitness. Various conventional fitness based selection functions exist, each providing a unique method of selecting individuals based on their fitness, fitness ranking within the population, and/or various other factors. However, the full space of selection algorithms is only limited by max algorithm size, and each possible selection algorithm is optimal for some EA configuration applied to a particular problem class. Therefore, improved performance is likely to be obtained by tuning an EA\u27s selection algorithm to the problem at hand, rather than employing a conventional selection function. This thesis details an investigation of the extent to which performance can be improved by tuning the selection algorithm. We do this by employing a Hyper-heuristic to explore the space of algorithms which determine the methods used to select individuals from the population. We show, with both a conventional EA and a Covariance Matrix Adaptation Evolutionary Strategy, the increase in performance obtained with a tuned selection algorithm, versus conventional selection functions. Specifically, we measure performance on instances from several benchmark problem classes, including separate testing instances to show generalization of the improved performance. This thesis consists of work that was presented at the Genetic and Evolutionary Computation Conference (GECCO) in 2018, as well as work that will be submitted to GECCO in 2019 --Abstract, page iii

Metaheuristic Design Patterns: New Perspectives for Larger-Scale Search Architectures

Design patterns capture the essentials of recurring best practice in an abstract form. Their merits are well established in domains as diverse as architecture and software development. They offer significant benefits, not least a common conceptual vocabulary for designers, enabling greater communication of high-level concerns and increased software reuse. Inspired by the success of software design patterns, this chapter seeks to promote the merits of a pattern-based method to the development of metaheuristic search software components. To achieve this, a catalog of patterns is presented, organized into the families of structural, behavioral, methodological and component-based patterns. As an alternative to the increasing specialization associated with individual metaheuristic search components, the authors encourage computer scientists to embrace the ‘cross cutting' benefits of a pattern-based perspective to optimization algorithms. Some ways in which the patterns might form the basis of further larger-scale metaheuristic component design automation are also discussed

Evolving Black-box Search Algorithms Employing Genetic Programming

Restricting the class of problems we want to perform well on allows Black Box Search Algorithms (BBSAs) specifi- cally tailored to that class to significantly outperform more general purpose problem solvers. However, the fields that encompass BBSAs, including Evolutionary Computing, are mostly focused on improving algorithm performance over increasingly diversified problem classes. By definition, the payoff for designing a high quality general purpose solver is far larger in terms of the number of problems it can ad- dress, than a specialized BBSA. This paper introduces a novel approach to creating tailored BBSAs through auto- mated design employing genetic programming. An exper- iment is reported which demonstrates its ability to create novel BBSAs which outperform established BBSAs includ- ing canonical evolutionary algorithms