How neutral networks influence evolvability
Complexity
Fitness landscapes and evolvability
Evolutionary Computation
Neutral Networks and Evolvability with Complex Genotype-Phenotype Mapping
ECAL '01 Proceedings of the 6th European Conference on Advances in Artificial Life
Through the Labyrinth Evolution Finds a Way: A Silicon Ridge
ICES '96 Proceedings of the First International Conference on Evolvable Systems: From Biology to Hardware
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
Smooth Operator? Understanding and Visualising Mutation Bias
ECAL '01 Proceedings of the 6th European Conference on Advances in Artificial Life
Redundant representations in evolutionary computation
Evolutionary Computation
Resilient Individuals Improve Evolutionary Search
Artificial Life
A quantitative study of neutrality in GP boolean landscapes
Proceedings of the 8th annual conference on Genetic and evolutionary computation
An empirical investigation of how and why neutrality affects evolutionary search
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Finding needles in haystacks is harder with neutrality
Genetic Programming and Evolvable Machines
Examining the Effect of Elitism in Cellular Genetic Algorithms Using Two Neighborhood Structures
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Neutrality and variability: two sides of evolvability in linear genetic programming
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Networks: An Introduction
Robustness, evolvability, and accessibility in linear genetic programming
EuroGP'11 Proceedings of the 14th European conference on Genetic programming
Examining mutation landscapes in grammar based genetic programming
EuroGP'11 Proceedings of the 14th European conference on Genetic programming
Accessibility and runtime between convex neutral networks
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
Bayesian Reasoning and Machine Learning
Bayesian Reasoning and Machine Learning
Robustness and evolvability of recombination in linear genetic programming
EuroGP'13 Proceedings of the 16th European conference on Genetic Programming
Genetic Programming and Evolvable Machines
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Redundancy is a ubiquitous feature of genetic programming (GP), with many-to-one mappings commonly observed between genotype and phenotype, and between phenotype and fitness. If a representation is redundant, then neutral mutations are possible. A mutation is phenotypically-neutral if its application to a genotype does not lead to a change in phenotype. A mutation is fitness-neutral if its application to a genotype does not lead to a change in fitness. Whether such neutrality has any benefit for GP remains a contentious topic, with reported experimental results supporting both sides of the debate. Most existing studies use performance statistics, such as success rate or search efficiency, to investigate the utility of neutrality in GP. Here, we take a different tack and use a measure of robustness to quantify the neutrality associated with each genotype, phenotype, and fitness value. We argue that understanding the influence of neutrality on GP requires an understanding of the distributions of robustness at these three levels, and of the interplay between robustness, evolvability, and accessibility amongst genotypes, phenotypes, and fitness values. As a concrete example, we consider a simple linear genetic programming system that is amenable to exhaustive enumeration and allows for the full characterization of these quantities, which we then relate to the dynamical properties of simple mutation-based evolutionary processes. Our results demonstrate that it is not only the distribution of robustness amongst phenotypes that affects evolutionary search, but also (1) the distributions of robustness at the genotypic and fitness levels and (2) the mutational biases that exist amongst genotypes, phenotypes, and fitness values. Of crucial importance is the relationship between the robustness of a genotype and its mutational bias toward other phenotypes.