Neutral networks in an evolutionary robotics search space

Smith, Tom; Husbands, Phil; O’Shea, Michael

doi:10.1109/cec.2001.934382

Cited by 28 publications

(23 citation statements)

References 17 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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“…Bifurcations are thus not rare when evolving robots and create discontinuities in the fitness values. Fitness plateaus are common in ER [166]. Typical ER fitness landscapes are then large, at least partly rugged and include plateaus.…”

Section: Premature Convergencementioning

confidence: 99%

Beyond black-box optimization: a review of selective pressures for evolutionary robotics

Doncieux

Mouret

2014

Evol. Intel.

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Evolutionary robotics is often viewed as the application of a family of black-box optimization algorithms -evolutionary algorithms -to the design of robots, or parts of robots. When considering evolutionary robotics as black-box optimization, the selective pressure is mainly driven by a user-defined, black-box fitness function, and a domain-independent selection procedure. However, most evolutionary robotics experiments face similar challenges in similar setups: the selective pressure, and, in particular, the fitness function, is not a pure user-defined black box. The present review shows that, because evolutionary robotics experiments share common features, selective pressures for evolutionary robotics are a subject of research on their own. The literature has been split into two categories: goal refiners, aimed at changing the definition of a good solution, and process helpers, designed to help the search process. Two subcategories are further considered: task-specific approaches, which require knowledge on how to solve the task and taskagnostic ones, which do not need it. Besides highlighting the diversity of the approaches and their respective goals, the present review shows that many task-agnostic process helpers have been proposed during the last years, thus bringing us closer to the goal of a fully automated robot behavior design process.

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“…So, they concluded that the presence of neutrality is not beneficial for evolutionary search. This line of thought was further investigated in [80] where Smith et al focused their attention on the population's dynamics to point out that when the fitness does not change, this is due to the presence of neutrality rather than to the population getting stuck in local optima.…”

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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Neutral networks in an evolutionary robotics search space

Cited by 28 publications

References 17 publications

Robustness, Evolvability, and Accessibility in Linear Genetic Programming

Robustness, Evolvability, and Accessibility in Linear Genetic Programming

Beyond black-box optimization: a review of selective pressures for evolutionary robotics

Neutrality in evolutionary algorithms… What do we know?

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