On neutral networks and evolvability

Ebner, Marc; Langguth, P.; Albert, JG; Shackleton, Mark; Shipman, Rob

doi:10.1109/cec.2001.934363

Cited by 52 publications

(34 citation statements)

References 13 publications

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“…Ebner et al [24], [25] argued that these types of mappings are particularly interesting since they seem to allow neutral networks that are intertwined with a high degree of connectivity. This property allows the finding of more species compared with other types of mappings.…”

Section: Previous Work On Neutralitymentioning

confidence: 99%

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The Effects of Constant and Bit-Wise Neutrality on Problem Hardness, Fitness Distance Correlation and Phenotypic Mutation Rates

Poli

Galván

2012

IEEE Trans. Evol. Computat.

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Kimura's neutral theory of evolution has inspired researchers from the evolutionary computation community to incorporate neutrality into Evolutionary Algorithms (EAs) in the hope that it can aid evolution. The effects of neutrality on evolutionary search have been considered in a number of studies, the results of which, however, have been highly contradictory. In this paper, we analyse the reasons for this and we make an effort to shed some light on neutrality by addressing them. We consider two very simple forms of neutrality: constant neutrality -a neutral network of constant fitness, identically distributed in the whole search space -and bit-wise neutrality, where each phenotypic bit is obtained by transforming a group of genotypic bits via an encoding function. We study these forms of neutrality both theoretically and empirically (both for standard benchmark functions and a class of random MAX-SAT problems) to see how and why they influence the behaviour and performance of a mutation-based EA. In particular, we analyse how the fitness distance correlation of landscapes changes under the effect of different neutral encodings and how phenotypic mutation rates vary as a function of genotypic mutation rates. Both help explain why the behaviour of a mutation-based EA may change so radically as problem, form of neutrality and mutation rate are varied.

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Section: Previous Work On Neutralitymentioning

confidence: 99%

“…In particular, they focused their attention on the RBN mapping proposed and studied in [22]- [24] and measured its influence on evolution using the rate of fitness increase. EAs and Hill-Climbers were used on three different problems to compare the performance obtained with and without the RBN mapping.…”

Section: Previous Work On Neutralitymentioning

confidence: 99%

The Effects of Constant and Bit-Wise Neutrality on Problem Hardness, Fitness Distance Correlation and Phenotypic Mutation Rates

Poli

Galván

2012

IEEE Trans. Evol. Computat.

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“…In [19,20], Ebner et al extended the previous investigation of [75,77]. For this purpose, the authors analysed the effects of the RBN mapping and the cellular automaton (both described previously) in the context of what they called phenotype-species mapping.…”

Section: Genotype-phenotype Mappings -Definitions and Propertiesmentioning

confidence: 99%

“…In particular, they focused their attention on the RBN mapping proposed and studied in [19,75,77] and measured its performance using the rate of fitness increase. Knowles and Watson used GAs and Hill-Climbing on three different problems free of neutrality to compare the performance obtained when RBN was and was not used.…”

Section: Criticism To Some Genotype-phenotype Mappingsmentioning

confidence: 99%

Neutrality in evolutionary algorithms… What do we know?

et al. 2011

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Publication informationEvolving Systems, 2 (3): 145-163Publisher Springer Abstract Over the last years, the effects of neutrality have attracted the attention of many researchers in the Evolutionary Algorithms (EAs) community. A mutation from one gene to another is considered as neutral if this modification does not affect the phenotype. This article provides a general overview on the work carried out on neutrality in EAs. Using as a framework the origin of neutrality and its study in different paradigms of EAs (e.g., Genetic Algorithms, Genetic Programming), we discuss the most significant works and findings on this topic. This work points towards open issues, which the community needs to address.

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“…The potential benefits of neutrality was subject of many researches in the biological science (Conrad, 1990;Hynen, 1996;Kimura, 1968). At the same time, the growing interest for the usage of this knowledge in evolutionary computation was raised (Barnett, 1998;Ebner et al, 2001). Toussaint & Igel (2002) dealt with the non-injectivity of genotype-phenotype mapping that is the main characteristic of this mapping.…”

Section: The Self-adaptive Evolutionary Algorithmsmentioning

confidence: 99%