Crossover in Grammatical Evolution
Genetic Programming and Evolvable Machines
Genotype-Phenotype-Mapping and Neutral Variation - A Case Study in Genetic Programming
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
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Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language
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ACM SIGEVOlution
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Genetic programming using genotype-phenotype mapping from linear genomes into linear phenotypes
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
An adverse interaction between crossover and restricted tree depth in genetic programming
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Foundations in Grammatical Evolution for Dynamic Environments
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A Field Guide to Genetic Programming
A Field Guide to Genetic Programming
Linear Genetic Programming
An analysis of the behaviour of mutation in grammatical evolution
EuroGP'10 Proceedings of the 13th European conference on Genetic Programming
An analysis of genotype-phenotype maps in grammatical evolution
EuroGP'10 Proceedings of the 13th European conference on Genetic Programming
Genotype-phenotype mapping in dynamic environments with grammatical evolution
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
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We present an analysis of how the genotype-phenotype map in Grammatical Evolution (GE) can effect performance on the Max Problem. Earlier studies have demonstrated a performance decrease for Position Independent Grammatical Evolution (πGE) in this problem domain. In πGE the genotype-phenotype map is changed so that the evolutionary algorithm controls not only what the next expansion will be but also the choice of what position in the derivation tree is expanded next. In this study we extend previous work and investigate whether the ability to change the order of expansion is responsible for the performance decrease or if the problem is simply that a certain order of expansion in the genotype-phenotype map is responsible. We conclude that the reduction of performance in the Max problem domain by πGE is rooted in the way the genotype-phenotype map and the genetic operators used with this mapping interact.