Through the labyrinth evolution finds a way: A silicon ridge

Harvey, Inman; Thompson, Adrian

doi:10.1007/3-540-63173-9_62

Cited by 75 publications

(52 citation statements)

References 10 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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“…According to Harvey and Thompson [38], improvements of fitness can occur during evolution in GAs even in the presence of a converged population. Furthermore, they claimed: "If a non-neutral genetic encoding (one which generates a non-neutral fitness landscape), with binary genotypes of length n, is modified by the addition of g extra redundant loci, then each phenotype will now be represented by 2 g points in genotype space instead of just one.…”

Section: Previous Work On Neutrality In Genetic Algorithmsmentioning

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 unused parts of the gene provide a neutral search space, which can have positive effects on evolvability. It has been shown that neutrality provides stability via preventing deleterious mutations, and helps evolution avoid local optima, thus creating a smoother search space [77], [78].…”

Section: Chemicalsmentioning

confidence: 99%

An Investigation of the Importance of Mechanisms and Parameters in a Multicellular Developmental System

Kuyucu

Trefzer

Miller

et al. 2011

IEEE Trans. Evol. Computat.

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Abstract-Multicellular organisms in biology possess invaluable characteristics, which artificial systems in engineering lack, such as adaptivity and robustness. Modeling biologically inspired multicellular developmental systems in evolutionary computation have been considered for a number of years, and there exist many developmental simulations that capture multicellular characteristics of biological organisms. However, the relative importance of many mechanisms in such models is still poorly understood. This paper undertakes a detailed investigation of the importance of many mechanisms and parameters on the organizational behavior of a gene regulatory network based artificial developmental system via classical pattern matching experiments. The work leads to an improved understanding of artificial multicellular development, which will assist in its utilization in the application of evolutionary computation. It may also provide a better understanding of mechanisms of biological development.

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Through the labyrinth evolution finds a way: A silicon ridge

Cited by 75 publications

References 10 publications

Robustness, Evolvability, and Accessibility in Linear Genetic Programming

Robustness, Evolvability, and Accessibility in Linear Genetic Programming

Neutrality in evolutionary algorithms… What do we know?

An Investigation of the Importance of Mechanisms and Parameters in a Multicellular Developmental System

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