Redundant Representations in Evolutionary Computation

Rothlauf, Franz; Goldberg, David E.

doi:10.1162/106365603322519288

Cited by 136 publications

(114 citation statements)

References 36 publications

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“…From the five problems that we have analysed, and using the three different definitions of neighbourhood denoted by Def 0 , Def 1 and Def 2 (see Sections 2.1 and 2.2), we have seen that the correct prediction was obtained more often when neighbourhood was defined with Def 1 which correspond to the definition given by Rothlauf in his original genotype-phenotype mapping studies using bitstrings [53].…”

Section: Discussionmentioning

confidence: 85%

“…It has been argued that locality is a key element in performing effective evolutionary search of landscapes [53]. According to Rothlauf [53], a representation that has high locality is necessary for efficient evolutionary search.…”

Section: Discussionmentioning

confidence: 99%

“…According to Rothlauf [53], a representation that has high locality is necessary for efficient evolutionary search. The opposite is true when a representation has low locality.…”

Section: Discussionmentioning

confidence: 99%

“…Rothlauf [53,56] has described and analysed the importance of locality in performing effective evolutionary search of landscapes. In EC, locality refers to how well neighbouring genotypes correspond to neighbouring phenotypes, and is useful as an indicator of problem difficulty.…”

Section: Introductionmentioning

confidence: 99%

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Defining locality as a problem difficulty measure in genetic programming

Galván

McDermott

O’Neill

et al. 2011

Genet Program Evolvable Mach

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Publication information Genetic Programming and Evolvable Machines, 12 (4): 365-401Publisher Springer Abstract A mapping is local if it preserves neighbourhood. In Evolutionary Computation, locality is generally described as the property that neighbouring genotypes correspond to neighbouring phenotypes. A representation has high locality if most genotypic neighbours are mapped to phenotypic neighbours. Locality is seen as a key element in performing effective evolutionary search. It is believed that a representation that has high locality will perform better in evolutionary search and the contrary is true for a representation that has low locality. When locality was introduced, it was the genotype-phenotype mapping in bitstring-based Genetic Algorithms which was of interest; more recently, it has also been used to study the same mapping in Grammatical Evolution. To our knowledge, there are few explicit studies of locality in Genetic Programming (GP). The goal of this paper is to shed some light on locality in GP and use it as an indicator of problem difficulty. Strictly speaking, in GP the genotype and the phenotype are not distinct. We attempt to extend the standard quantitative definition of genotype-phenotype locality to the genotype-fitness mapping by considering three possible definitions. We consider the effects of these definitions in both continuous-and discrete-valued fitness functions. We compare three different GP representations (two of them induced by using different function sets and the other using a slightly different GP encoding) and six different mutation operators. Results indicate that one definition of locality is better in predicting performance.

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Section: Discussionmentioning

confidence: 85%

Section: Discussionmentioning

confidence: 99%

“…According to Rothlauf [53], a representation that has high locality is necessary for efficient evolutionary search. The opposite is true when a representation has low locality.…”

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Defining locality as a problem difficulty measure in genetic programming

Galván

McDermott

O’Neill

et al. 2011

Genet Program Evolvable Mach

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“…Another example of redundant mappings that improve performance of genetic algorithms is shown in Rothlauf and Goldberg [33]. This paper explores three different representations for the same scheduling problem in real-time parallel applications, which allow failing to meet certain deadlines or deliverables.…”

Section: Representation and Genotype-phenotype Mappingmentioning

confidence: 99%

Brief Review of Techniques Used to Develop Adaptive Evolutionary Algorithms

Bonilla-Vera¹,

Mora-Vargas²,

González-Mendoza³

et al. 2017

Open Cyber

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This paper presents a brief review of techniques used to allow evolutionary algorithms to adapt to optimization problems in dynamic environments, through exploration of the control parameters of genetic algorithms as well as genotypic interpreters. A description of some of the most used evolutionary techniques is included, with major emphasis on genetic algorithms and their relationship with the problem of adaptation to the environment. The article also discusses state used models to tackle these kinds of problems, including self-adaptation and genotype-phenotype mapping.

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Compact Genetic Codes as a Search Strategy of Evolutionary Processes

Toussaint

2005

Foundations of Genetic Algorithms

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Abstract. The choice of genetic representation crucially determines the capability of evolutionary processes to find complex solutions in which many variables interact. The question is how good genetic representations can be found and how they can be adapted online to account for what can be learned about the structure of the problem from previous samples. We address these questions in a scenario that we term indirect Estimation-of-Distribution: We consider a decorrelated search distribution (mutational variability) on a variable length genotype space. A one-to-one encoding onto the phenotype space then needs to induce an adapted phenotypic variability incorporating the dependencies between phenotypic variables that have been observed successful previously. Formalizing this in the framework of Estimation-of-Distribution Algorithms, an adapted phenotypic variability can be characterized as minimizing the Kullback-Leibler divergence to a population of previously selected individuals (parents). Our core result is a relation between the Kullback-Leibler divergence and the description length of the encoding in the specific scenario, stating that compact codes provide a way to minimize this divergence. A proposed class of Compression Evolutionary Algorithms and preliminary experiments with an L-system compression scheme illustrate the approach. We also discuss the implications for the self-adaptive evolution of genetic representations on the basis of neutrality (σ-evolution) towards compact codes.

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Redundant Representations in Evolutionary Computation

Cited by 136 publications

References 36 publications

Defining locality as a problem difficulty measure in genetic programming

Defining locality as a problem difficulty measure in genetic programming

Brief Review of Techniques Used to Develop Adaptive Evolutionary Algorithms

Compact Genetic Codes as a Search Strategy of Evolutionary Processes

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