Guest Editorial: special issue on evolutionary optimisation and learning

Abstract

Problem-solving techniques based on simulated evolution, particularly those inspired by the Darwinian theory of natural selection and biological evolution, have attracted great attention. These evolutionary optimisation and learning algorithms differ from the traditional optimisation and learning methods in that they involve a search from a population of solutions. Over the past decades, evolutionary optimisation and learning algorithms have been successfully applied to a wide variety of real-world problems. This special issue brings together some recent works from a wide range of topics concerning evolutionary optimisation and learning, including Differential Evolution, Cellular Genetic Algorithms, Particle Swarm Optimisation, Artificial Immune System, Coevolution, Crossover Analysis, Dynamic Combinatorial Optimisation, and Multimodal Optimisation. The eight papers included in this special issue originated from SEAL’08 (7th International Conference on Simulated Evolution and Learning), but have been substantially extended and revised from the conference version. These further extended papers were again rigorously reviewed in two rounds by at least three anonymous reviewers. The paper by Lehre and Yao investigates the impact of the parameter settings particularly the acceptance criterion in evolutionary algorithms (EAs) and the crossover operator SSGA when computing unique input output sequences (UIOs) from finite state machines. The objective is to identify simple, archetypical cases where these EA parameter settings have a particularly strong effect on the runtime of the algorithm. This result shows that minor modification in evolutionary algorithms can have an exponentially large runtime impact for computing UIOs and that when computing UIOs, the crossover operator can be essential, and simple EAs can be inefficient. The paper by Ronkknen et al. describes a software framework for generating multimodal test functions. The framework provides an easy way to construct parameterisable functions and offers an environment for testing multimodal optimisation algorithms. A number of function families with different characteristics are included. The framework implements new parameterisable function families for generating desired landscapes. In addition, the framework implements a selection of well-known test functions from the literature, which can be rotated and stretched. The software module can be easily imported into any optimisation algorithm implementation compatible with the C-programming language. As an example, eight optimisation approaches are compared by their ability to locate several global optima over a set of 16 functions with different properties generated by the proposed module. The paper by Dick and Whigham introduces two modifications to the local sharing method for multimodal optimisation. The first alters local sharing so that parent selection and fitness sharing operate at two different spatial levels: parent selection is performed within small demes, M. Zhang (&) School of Engineering and Computer Science, Victoria University of Wellington, PO Box 600, Wellington, New Zealand e-mail: mengjie.zhang@ecs.vuw.ac.nz