PrefaceDynamic environments abound and offer particular challenges for all optimisation and problem solving methods. A well-known strategy for survival in dynamic environments is to adopt a population-based approach. Rather than maintaining a single candidate solution, a population of candidate solutions is employed. This allows a diversity of potential solutions to be maintained, which increases the likelihood that a sufficient solution exists at any point in time to ensure the survival of the population in the long term. Dynamic environments can exhibit different types of change that can be abrupt and random, cyclical, or the product of complex relationships. The changes might range from relatively small smooth transitions to substantial perturbations in all aspects of the domain.Natural Computing (NC) has given rise to a family of population-based algorithms that exhibit varying degrees of success in solving problems in dynamic environments. It is natural to turn to algorithms which are inspired by the natural world when one wishes to solve problems in the natural world. In particular, biological evolution has given rise to effective problem solvers which survive in complex dynamic environments. Without natural evolution, the inspriation for evolutionary compuation, we would not have any of the other NC algorithms such as neurocomputing, immunocomputing, sociocomputing and grammatical and developmental computing; they are inspired by the products of the biological evolutionary process acting in a dynamic environment.In this book we focus on the first steps in the extension of a grammarbased form of Genetic Programming, Grammatical Evolution, in order to improve its ability to solve problems in dynamic environments. A relatively recent, powerful, addition to the stable of Evolutionary Computation, Grammatical Evolution (GE) adopts BNF grammars for the evolution of variable length programs. Thus far, there has been little study of the utility of GE in dynamic environments. Foundations in Grammatical Evolution for Dynamic Environments is the second book to be published on Grammatical Evolution, and it has been six years since Grammatical Evolution: VI Preface Evolutionary Automatic Programming in an Arbitrary Language appeared.A comprehensive analysis of prior work in EC and GE in the context of dynamic environments is presented. From this, it is seen that GE offers substantial potential due to the flexibility provided by the BNF grammar and the many-to-one genotype-to-phenotype mapping.Subsequently, novel methods of constant creation are introduced that incorporate greater levels of latent evolvability through the use of BNF grammars. These methods are demonstrated to be more accurate and adaptable than the standard methods adopted.Through placing GE in the context of a dynamic real-world problem, the trading of financial indices, phenotypic diversity is demonstrated to be a function of the fitness landscape. That is, phenotypic entropy fluctuates with the universe of potentially fit solutions. Evidence is also...

This paper investigates the applicability of Genetic Programming type systems to dynamic game environments. Grammatical Evolution was used to evolve Behaviour Trees, in order to create controllers for the Mario AI Benchmark. The results obtained reinforce the applicability of evolutionary programming systems to the development of artificial intelligence in games, and in dynamic systems in general, illustrating their viability as an alternative to more standard AI techniques.

Abstract. This study examines Social Programming, that is, the construction of programs using a Social Swarm algorithm based on Particle Swarm Optimization. Each individual particle represents choices of program construction rules, where these rules are specified using a Backus-Naur Form grammar. This study represents the first instance of a Particle Swarm Algorithm being used to generate programs. A selection of benchmark problems from the field of Genetic Programming are tackled and performance is compared to Grammatical Evolution. The results demonstrate that it is possible to successfully generate programs using the Grammatical Swarm technique. An analysis of the Grammatical Swarm approach is presented on the dynamics of the search. It is found that restricting the search to the generation of complete programs, or with the use of a ratchet constraint forcing individuals to move only if a fitness improvement has been found, can have detrimental consequences for the swarms performance and dynamics.

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Abstract-Computer games are highly dynamic environments, where players are faced with a multitude of potentially unseen scenarios. In this article, AI controllers are applied to the Mario AI Benchmark platform, by using the Grammatical Evolution system to evolve Behavior Tree structures. These controllers are either evolved to both deal with navigation and reactiveness to elements of the game, or used in conjunction with a dynamic A* approach. The results obtained highlight the applicability of Behavior Trees as representations for evolutionary computation, and their flexibility for incorporation of diverse algorithms to deal with specific aspects of bot control in game environments.

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Publication informationInternational Journal of Design Engineering, 3 (1): 4-24 Publisher Inderscience EnterprisesLink to online version http://dx. Martin Hemberg is a post-doctoral researcher at the Department of Ophthalmology at Children's Hospital Boston. He obtained is PhD from Imperial College London and he has also worked at the Architectural Association in London. His primary research interests include matheEvolutionary design using grammatical evolution and shape grammars 3 matical and computational models of gene expression

Publication informationEvolving Systems, 2 (3): 145-163Publisher Springer Abstract Over the last years, the effects of neutrality have attracted the attention of many researchers in the Evolutionary Algorithms (EAs) community. A mutation from one gene to another is considered as neutral if this modification does not affect the phenotype. This article provides a general overview on the work carried out on neutrality in EAs. Using as a framework the origin of neutrality and its study in different paradigms of EAs (e.g., Genetic Algorithms, Genetic Programming), we discuss the most significant works and findings on this topic. This work points towards open issues, which the community needs to address.