Genetic Programming and Data Structures: Genetic Programming + Data Structures = Automatic Programming!
Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
From Recombination of Genes to the Estimation of Distributions II. Continuous Parameters
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
From Recombination of Genes to the Estimation of Distributions I. Binary Parameters
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Applying price's equation to survival selection
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
A new methodology for the GP theory toolbox
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Review of phenotypic diversity formulations for diagnostic tool
Applied Soft Computing
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Customizing and evolutionary algorithm (EA) for a new or unusual problem can seem relatively simple as long as one can devise an appropriate representation and reproductive operators to modify it. Unfortunately getting a customized EA to produce high quality results in a reasonable amount of time can be quite challenging. There is little guidance available to help practitioners deal with this issue. Most evolutionary computation (EC) theory is only applicable to specific representations, or assumes knowledge of the fitness function, such as the location of optima. We are developing an approach based on theory from the biology community to address this problem. Multivariate quantitative genetics theory characterizes evolving populations as multivariate probability distributions of phenotypic traits. Some advantages it offers are a degree of independence from the underlying representation, and useful concepts such as phenotypic heritability. Re-working the quantitative genetics equations, we expose an additional term that we call "perturbation". We believe that perturbation and heritability provide quantitative measures of the exploration and exploitation, and that practitioners can use these to identify and diagnose imbalances in customized reproductive operators. To illustrate, we use these tools to diagnose problems with a standard recombination operator for a Pittsburgh approach classifier system. With this knowledge we develop a new, more balanced, recombination operator, and show that its use leads to significantly better results.