Work

Probabilistic Structured Grammatical Evolution

Year: 2022

Type: conference-paper

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.

Source: IEEE Congress on Evolutionary Computation (CEC)

DOI: 10.1109/cec55065.2022.9870397

Authors: Jessica Megane, Nuno Lourenco, Penousal Machado

Institution: University of Coimbra

Language: English

Topic: Evolutionary Algorithms and Applications

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: Federación Española de Enfermedades Raras, Fundação para a Ciência e a Tecnologia, European Regional Development Fund