Titlebook: Genetic Programming; 6th European Confere Conor Ryan,Terence Soule,Ernesto Costa Conference proceedings 2003 Springer-Verlag Berlin Heidelb

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Parallel Programs Are More Evolvable than Sequential Programsams running on a .. We carried out a series of experiments on GPP with different number of ALUs. We observed that parallel programs are more evolvable than sequential programs. For example, in the Fibonacci sequence regression experiment, evolving a 1-ALU sequential program requires 51 times on aver

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Genetic Programming with Meta-search: Searching for a Successful Population within the Classification the case that a number of runs must be performed to ensure that an effective solution is found. This paper introduces several methods which attempt to better utilise the computational resources spent on performing a number of independent GP runs. Termed metasearch strategies, these methods seek to

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How Functional Dependency Adapts to Salience Hierarchy in the GAuGE Systemcess. By specifying both the position and the value of each gene, it has the potential to group important data together in the genotype string, to prevent it from being broken up and disrupted during the evolution process. To test this ability, GAuGE was applied to a set of problems with exponential

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More on Computational Effort Statistics for Genetic Programmingnt-selection (steady-state) GP and show why, in both cases, the measured value of the . often differs from its theoretical counterpart. It is discussed how systematic estimation errors are introduced by a low number of experiments. Two reasons examined are the number of unsuccessful experiments and

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Analysis of a Digit Concatenation Approach to Constant Creationing constants in Grammatical Evolution. Digit concatenation involves creating constants (either whole or real numbers) by concatenating digits to form a single value. The two methods are compared using three different problems, which are finding a static real constant, finding dynamic real constants

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Maximum Homologous Crossover for Linear Genetic Programmingmpts to preserve similar structures from parents, by aligning them according to their homology, thanks to an algorithm used in Bio-Informatics. To highlight disruptive effects of crossover operators, we introduce the Royal Road landscapes and the Homology Driven Fitness problem, for Linear Genetic P