A connectionist machine for genetic hillclimbing
A connectionist machine for genetic hillclimbing
Representations for Genetic and Evolutionary Algorithms
Representations for Genetic and Evolutionary Algorithms
The Use of Neutral Genotype-Phenotype Mappings for Improved Evolutionary Search
BT Technology Journal
On the Utility of Redundant Encodings in Mutation-Based Evolutionary Search
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
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
Neutrality and the Evolvability of Boolean Function Landscape
EuroGP '01 Proceedings of the 4th European Conference on Genetic Programming
Neutrality: a necessity for self-adaptation
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
The gambler's ruin problem, genetic algorithms, and the sizing of populations
Evolutionary Computation
When to use bit-wise neutrality
Natural Computing: an international journal
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This paper investigates how the use of the trivial voting (TV) mapping influences the performance of genetic algorithms (GAs). The TV mapping is a redundant representation for binary phenotypes. A population sizing model is presented that quantitatively predicts the influence of the TV mapping and variants of this encoding on the performance of GAs. The results indicate that when using this encoding GA performance depends on the influence of the representation on the initial supply of building blocks. Therefore, GA performance remains unchanged if the TV mapping is uniformly redundant that means on average a phenotype is represented by the same number of genotypes. If the optimal solution is overrepresented, GA performance increases, whereas it decreases if the optimal solution is underrepresented. The results show that redundant representations like the TV mapping do not increase GA performance in general. Higher performance can only be achieved if there is specific knowledge about the structure of the optimal solution that can beneficially be used by the redundant representation.