An Ecological Approach to Measuring Locality in Linear Genotype to Phenotype Maps

Seaton, Tom; Miller, Julian F.; Clarke, Tim

doi:10.1007/978-3-642-29139-5_15

Cited by 7 publications

(3 citation statements)

References 19 publications

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“…The lack of correlation between the size of change in syntax against the behaviour of the tree is measured in terms of locality [10][11][12]. Locality of search operators in GP has been noted in most representation times, including parse trees, grammatical evolution and Cartesian representations [12][13][14].…”

Section: Semantic Methods In Gpmentioning

confidence: 99%

An Ensemble Learning Interpretation of Geometric Semantic Genetic Programming

Dick

2023

Preprint

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Geometric semantic genetic programming (GSGP) is a variant of genetic programming (GP) that directly searches the semantic space of programs to produce candidate solutions. GSGP has shown considerable success in improving the performance of GP in terms of program correctness, however this comes at the expense of exponential program growth. Subsequent attempts to address this growth have not fully-exploited the fact that GSGP searches by producing linear combinations of existing solutions. This paper examines this property of GSGP and frames the method as an ensemble learning defining mutation and crossover as examples of boosting and stacking, respectively. The ensemble interpretation allows for simple integration of regularisation techniques that significantly reduce the size of the resultant programs. Additionally, this paper examines the quality of parse tree base learners within this ensemble interpretation of GSGP and suggests that future research could substantially improve the quality of GSGP by examining more effective initialisation techniques. The resulting ensemble learning interpretation leads to variants of GSGP that substantially improve upon the performance of traditional GSGP in regression contexts, and produce a method that frequently outperforms gradient boosting.

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Section: Semantic Methods In Gpmentioning

confidence: 99%

An Ensemble Learning Interpretation of Geometric Semantic Genetic Programming

Dick

2023

Preprint

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“…A parameterised technique similar to the methods of Nguyen [7] was used, which has been previously applied to CGP to introduce a syntactic bias in [8]. A metric approximating the semantic difference between programs was defined for each problem, d GP −GT G , d SC and d GoT respectively:…”

Section: Semantic Biasmentioning

confidence: 99%

“…This empirical work focuses on one possible factor, semantic bias. Research on semantics and the genotype-phenotype map in GP has been a prominent area since GP's inception [6][7][8], but it is not clear how previous findings in classical single population GP extend to coevolutionary systems. The experiments described here examine the effect of local and non-local semantic topology on disengagement, cycling and performance in a coevolutionary GP system.…”

Section: Introductionmentioning

confidence: 99%

Semantic Bias in Program Coevolution

Seaton

Miller

Clarke

2013

Lecture Notes in Computer Science

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Abstract. We investigate the occurrence of two pathological coevolutionary behaviours, specifically disengagement and cycling, in GP systems. An empirical analysis is carried out using constructed GP problems and a historical pursuit and evasion task, the Game of Tag. The effect of semantic bias on the likelihood of pathologies and performance in a coevolutionary context is examined. We present findings correlating semantic locality in the genotype to phenotype map to disengagement and cycling in a minimal competitive coevolutionary algorithm.

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An ensemble learning interpretation of geometric semantic genetic programming

Dick

2024

Genet Program Evolvable Mach

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Geometric semantic genetic programming (GSGP) is a variant of genetic programming (GP) that directly searches the semantic space of programs to produce candidate solutions. GSGP has shown considerable success in improving the performance of GP in terms of program correctness, however this comes at the expense of exponential program growth. Subsequent attempts to address this growth have not fully-exploited the fact that GSGP searches by producing linear combinations of existing solutions. This paper examines this property of GSGP and frames the method as an ensemble learning approach by redefining mutation and crossover as examples of boosting and stacking, respectively. The ensemble interpretation allows for simple integration of regularisation techniques that significantly reduce the size of the resultant programs. Additionally, this paper examines the quality of parse tree base learners within this ensemble learning interpretation of GSGP and suggests that future research could substantially improve the quality of GSGP by examining more effective initialisation techniques. The resulting ensemble learning interpretation leads to variants of GSGP that substantially improve upon the performance of traditional GSGP in regression contexts, and produce a method that frequently outperforms gradient boosting.

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An Ecological Approach to Measuring Locality in Linear Genotype to Phenotype Maps

Cited by 7 publications

References 19 publications

An Ensemble Learning Interpretation of Geometric Semantic Genetic Programming

An Ensemble Learning Interpretation of Geometric Semantic Genetic Programming

Semantic Bias in Program Coevolution

An ensemble learning interpretation of geometric semantic genetic programming

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