A comparison of fitness-case sampling methods for symbolic regression with genetic programming

International audienc

Searching for Novel Classifiers

Abstract. Natural evolution is an open-ended search process without an a priori fitness function that needs to be optimized. On the other hand, evolutionary algorithms (EAs) rely on a clear and quantitative objective. The Novelty Search algorithm (NS) substitutes fitness-based selection with a novelty criteria; i.e., individuals are chosen based on their uniqueness. To do so, individuals are described by the behaviors they exhibit, instead of their phenotype or genetic content. NS has mostly been used in evolutionary robotics, where the concept of behavioral space can be clearly defined. Instead, this work applies NS to a more general problem domain, classification. To this end, two behavioral descriptors are proposed, each describing a classifier’s performance from two different perspectives. Experimental results show that NS-based search can be used to derive effective classifiers. In particular, NS is best suited to solve difficult problems, where exploration needs to be encouraged and maintained

Improving the generalisation ability of genetic programming with semantic similarity based crossover

European Conference on Genetic Programming, Istanbul Turkey, 7-9 April 2010This paper examines the impact of semantic control on the ability of Genetic Programming (GP) to generalise via a semantic based crossover operator (Semantic Similarity based Crossover - SSC). The use of validation sets is also investigated for both standard crossover and SSC. All GP systems are tested on a number of real-valued symbolic regression problems. The experimental results show that while using validation sets barely improve generalisation ability of GP, by using semantics, the performance of Genetic Programming is enhanced both on training and testing data. Further recorded statistics shows that the size of the evolved solutions by using SSC are often smaller than ones obtained from GP systems that do not use semantics. This can be seen as one of the reasons for the success of SSC in improving the generalisation ability of GP.Irish Research Council for Science, Engineering and TechnologyEmbargo until April 2011 - AV 1/11/2010 ti, ke - TS 18/11/1

Preliminary Study of Bloat in Genetic Programming with Behavior-Based Search

Abstract. Bloat is one of the most interesting theoretical problems in genetic programming (GP), and one of the most important pragmatic limitations in the development of real-world GP solutions. Over the years, many theories regarding the causes of bloat have been proposed and a variety of bloat control methods have been developed. It seems that one of the underlying causes of bloat is the search for fitness; as the fitness-causes-bloat theory states, selective bias towards fitness seems to unavoidably lead the search towards programs with a large size. Intuitively, however, abandoning fitness does not appear to be an option. This paper, studies a GP system that does not require an explicit fitness function, instead it relies on behavior-based search, where programs are described by the behavior they exhibit and selective pressure is biased towards unique behaviors using the novelty search algorithm. Initial results are encouraging, the average program size of the evolving population does not increase with novelty search; i.e., bloat is avoided by focusing on novelty instead of quality