Probabilistic Structured Grammatical Evolution

TL;DR

This paper introduces Probabilistic Structured Grammatical Evolution (PSGE), a novel method combining structured and probabilistic grammar-based genetic programming techniques, which outperforms existing approaches on multiple benchmark problems.

Contribution

The paper presents PSGE, a new hybrid approach that integrates structured and probabilistic grammars, improving solution quality in grammar-based genetic programming.

Findings

PSGE outperforms standard GE on all benchmarks.

PSGE surpasses PGE on 4 out of 6 problems.

Probabilistic and structured integration enhances search effectiveness.

Abstract

The grammars used in grammar-based Genetic Programming (GP) methods have a significant impact on the quality of the solutions generated since they define the search space by restricting the solutions to its syntax. In this work, we propose Probabilistic Structured Grammatical Evolution (PSGE), a new approach that combines the Structured Grammatical Evolution (SGE) and Probabilistic Grammatical Evolution (PGE) representation variants and mapping mechanisms. The genotype is a set of dynamic lists, one for each non-terminal in the grammar, with each element of the list representing a probability used to select the next Probabilistic Context-Free Grammar (PCFG) derivation rule. PSGE statistically outperformed Grammatical Evolution (GE) on all six benchmark problems studied. In comparison to PGE, PSGE outperformed 4 of the 6 problems analyzed.