How neutral networks influence evolvability

Ebner, Marc; Shackleton, Mark; Shipman, Rob

doi:10.1002/cplx.10021

Cited by 85 publications

(88 citation statements)

References 37 publications

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“…Conflicting opinions have been proposed with regard to the effects of neutrality on evolutionary search. While some studies have found no benefit [2,3,4], others have claimed that neutrality provides a buffer against deleterious genetic perturbation [5,6] and reduces the risk of premature convergence through an expansion of the search space [7,8]. As argued in [9], the lack of consensus regarding the benefits of neutrality largely stem from the overly complex problems, representations, and search algorithms used in these analyses, which make it difficult to tease apart the effects of neutrality from other confounding factors.…”

Section: Introductionmentioning

confidence: 99%

Robustness, Evolvability, and Accessibility in Linear Genetic Programming

Payne

Banzhaf

et al. 2011

Lecture Notes in Computer Science

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Abstract. Whether neutrality has positive or negative effects on evolutionary search is a contentious topic, with reported experimental results supporting both sides of the debate. Most existing studies use performance statistics, e.g., success rate or search efficiency, to investigate if neutrality, either embedded or artificially added, can benefit an evolutionary algorithm. Here, we argue that understanding the influence of neutrality on evolutionary optimization requires an understanding of the interplay between robustness and evolvability at the genotypic and phenotypic scales. 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 properties. We adopt statistical measurements from RNA systems to quantify robustness and evolvability at both genotypic and phenotypic levels. Using an ensemble of random walks, we demonstrate that the benefit of neutrality crucially depends upon its phenotypic distribution.

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Section: Introductionmentioning

confidence: 99%

Robustness, Evolvability, and Accessibility in Linear Genetic Programming

Payne

Banzhaf

et al. 2011

Lecture Notes in Computer Science

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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%

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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“…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%

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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How neutral networks influence evolvability

Abstract: Evolutionary algorithms apply the process of variation, reproduction, and selection to look for an individual

Cited by 85 publications

References 37 publications

Robustness, Evolvability, and Accessibility in Linear Genetic Programming

Robustness, Evolvability, and Accessibility in Linear Genetic Programming

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

Neutrality in evolutionary algorithms… What do we know?

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