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Unveiling the properties of structured grammatical evolution

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Abstract

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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Notes

  1. In the previous problems, optimization designates the step where the GE algorithms are applied. For predictive modeling tasks we adopt the standard terms from the ML community: training/generalization.

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Acknowledgments

The first author is funded by Fundação para a Ciência e Tecnologia (FCT), Portugal, under the Grant SFRH/BD/79649/2011. The authors would like to thank the anonymous reviewers for their insightful comments that helped to improve our work.

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Correspondence to Nuno Lourenço.

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Lourenço, N., Pereira, F.B. & Costa, E. Unveiling the properties of structured grammatical evolution. Genet Program Evolvable Mach 17, 251–289 (2016). https://doi.org/10.1007/s10710-015-9262-4

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  • DOI: https://doi.org/10.1007/s10710-015-9262-4

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