Positional Effect of Crossover and Mutation in Grammatical Evolution

Castle, Tom; Johnson, Colin G.

doi:10.1007/978-3-642-12148-7_3

Cited by 18 publications

(15 citation statements)

References 8 publications

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“…In [6], the authors present an experimental study that identifies a positive correlation between the locus where variation operators are applied and their ability to change the phenotype. Results show that the application of crossover and mutation to the beginning of the genotype promotes low-locality, as it may alter the interpretation of all the remaining codons.…”

Section: Representation and Search Operatorsmentioning

confidence: 98%

Unveiling the properties of structured grammatical evolution

Lourenço

Pereira

Costa

2016

Genet Program Evolvable Mach

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Structured grammatical evolution (SGE) is a new genotypic representation for grammatical evolution (GE). It comprises a hierarchical organization of the genes, where each locus is explicitly linked to a non-terminal of the grammar being used. This one-to-one correspondence ensures that the modification of a gene does not affect the derivation options of other non-terminals. We present a comprehensive set of optimization results obtained with problems from three different categories: symbolic regression, path finding, and predictive modeling. In most of the situations SGE outperforms standard GE, confirming the effectiveness of the new representation. To understand the reasons for SGE enhanced performance, we scrutinize its main features. We rely on a set of static measures to model the interactions between the representation and variation operators and assess how they influence the interplay between the genotype-phenotype spaces. The study reveals that the structured organization of SGE promotes an increased locality and is less redundant than standard GE, thus fostering an effective exploration of the search space.

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Section: Representation and Search Operatorsmentioning

confidence: 98%

Unveiling the properties of structured grammatical evolution

Lourenço

Pereira

Costa

2016

Genet Program Evolvable Mach

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“…20.3.2 searched a space defined by a grammar, but the grammar in question was essentially defined by the type of information involved. Grammatical evolution (O'Neill 2003(O'Neill , 2012 could be used to explore more varied kinds of grammar, although there are open questions about how best to conduct the search through a grammar-defined space (Castle and Johnson 2010).…”

Section: Search Methodsmentioning

confidence: 99%

Hyper-heuristics

Ross¹

2013

Search Methodologies

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“…The boolean parity problems are widely used as a benchmark task in the GP literature [1,5,19]. However, they have only occasionally been tackled in the general form; for all values of n [18,20].…”

Section: Even-n-paritymentioning

confidence: 99%

Evolving High-Level Imperative Program Trees with Strongly Formed Genetic Programming

Castle

Johnson

2012

Lecture Notes in Computer Science

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We present a set of extensions to Montana's popular Strongly Typed Genetic Programming system that introduce constraints on the structure of program trees. It is demonstrated that these constraints can be used to evolve programs with a naturally imperative structure, using common high-level imperative language constructs such as loops. A set of three problems including factorial and the general even-n-parity problem are used to test the system. Experimental results are presented which show success rates and required computational effort that compare favourably against other systems on these problems, while providing support for this imperative structure.

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Positional Effect of Crossover and Mutation in Grammatical Evolution

Cited by 18 publications

References 8 publications

Unveiling the properties of structured grammatical evolution

Unveiling the properties of structured grammatical evolution

Hyper-heuristics

Evolving High-Level Imperative Program Trees with Strongly Formed Genetic Programming

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