On the Locality of Standard Search Operators in Grammatical Evolution

Thorhauer, Ann; Rothlauf, Franz

doi:10.1007/978-3-319-10762-2_46

Cited by 31 publications

(12 citation statements)

References 19 publications

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“…Empirical results obtained in two optimization problems reveal that the locality of the genotypephenotype mapping is low, as many genotypic neighbors originate highly dissimilar phenotypes. Recently Thorhauer et al [36] extended the previous work and compared the locality of the standard GE operators with standard GP. They analyzed the locality on problems that rely on binary trees, by performing random walks through the search space and measuring the distance between parents and offspring.…”

Section: Representation and Search Operatorsmentioning

confidence: 96%

“…Additionally there are several studies that aim to gain insight on how GE explores the search space [32,36]. The contributions directly related with the current work are briefly reviewed in the next sections.…”

Section: Related Workmentioning

confidence: 99%

“…GE has some known issues related with locality and redundancy [17,32,36]. A representation is said redundant when several different genotypes correspond to one phenotype.…”

Section: Introductionmentioning

confidence: 99%

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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: 96%

Section: Related Workmentioning

confidence: 99%

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Unveiling the properties of structured grammatical evolution

Lourenço

Pereira

Costa

2016

Genet Program Evolvable Mach

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“…The drawbacks commonly pointed to GE are low locality and high redundancy [23,24]. The locality measures how the changes in the genotype impact the phenotype.…”

Section: Dynamic Structured Grammatical Evolutionmentioning

confidence: 99%

Evolution of Scikit-Learn Pipelines with Dynamic Structured Grammatical Evolution

Assunção

Lourenço

Ribeiro

et al. 2020

Lecture Notes in Computer Science

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The deployment of Machine Learning (ML) models is a difficult and time-consuming job that comprises a series of sequential and correlated tasks that go from the data pre-processing, and the design and extraction of features, to the choice of the ML algorithm and its parameterisation. The task is even more challenging considering that the design of features is in many cases problem specific, and thus requires domain-expertise. To overcome these limitations Automated Machine Learning (AutoML) methods seek to automate, with few or no human-intervention, the design of pipelines, i.e., automate the selection of the sequence of methods that have to be applied to the raw data. These methods have the potential to enable non-expert users to use ML, and provide expert users with solutions that they would unlikely consider. In particular, this paper describes AutoML-DSGE -a novel grammar-based framework that adapts Dynamic Structured Grammatical Evolution (DSGE) to the evolution of Scikit-Learn classification pipelines. The experimental results include comparing AutoML-DSGE to another grammar-based AutoML framework, Resilient Classification Pipeline Evolution (RECIPE), and show that the average performance of the classification pipelines generated by AutoML-DSGE is always superior to the average performance of RECIPE; the differences are statistically significant in 3 out of the 10 used datasets.

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“…One crossover simply exchanges integers between crossover points on the genotype. Here, while the interpretation step raises some issues of locality, the grammar and the rewriting assure crossover will result in syntactically valid ospring [19]. Others operate on the derivation tree [3] Like in GP, though in grammatical form in GE, the abstraction of functions and terminals, i.e.…”

Section: Grammatical Evolutionmentioning

confidence: 99%

On domain knowledge and novelty to improve program synthesis performance with grammatical evolution

Hemberg

Kelly

O’Reilly

2019

Proceedings of the Genetic and Evolutionary Computation Conference

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Programmers solve coding problems with the support of both programming and problem specic knowledge. They integrate this domain knowledge to reason by computational abstraction. Correct and readable code arises from sound abstractions and problem solving. We attempt to transfer insights from such human expertise to genetic programming (GP) for solving automatic program synthesis. We draw upon manual and non-GP Articial Intelligence methods to extract knowledge from synthesis problem denitions to guide the construction of the grammar that Grammatical Evolution uses and to supplement its tness function. We examine the impact of using such knowledge on 21 problems from the GP program synthesis benchmark suite. Additionally, we investigate the compounding impact of this knowledge and novelty search. The resulting approaches exhibit improvements in accuracy on a majority of problems in the eld's benchmark suite of program synthesis problems. CCS CONCEPTS • Computing methodologies → Multi-agent systems;

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On the Locality of Standard Search Operators in Grammatical Evolution

Cited by 31 publications

References 19 publications

Unveiling the properties of structured grammatical evolution

Unveiling the properties of structured grammatical evolution

Evolution of Scikit-Learn Pipelines with Dynamic Structured Grammatical Evolution

On domain knowledge and novelty to improve program synthesis performance with grammatical evolution

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