Genetic Algorithm with Modified Reproduction Operators for Grammatical Inference
Authors: Pandey, H.M.
Journal: GECCO 2024 Companion - Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion
Pages: 2135-2138
DOI: 10.1145/3638530.3654151
Abstract:A grammatical inference (GI) algorithm is proposed, that utilizes a Genetic Algorithm (GA), in conjunction with a pushdown automaton (PDA) and the principle of minimum description length (MDL). GI is a methodology to infer context-free grammars (CFGs) from training data. It has wide applicability across many different fields, including natural language processing, language design, and software engineering. GAs is a search methodology that has been used in many domains and we utilize GAs as our primary search algorithm. The proposed algorithm incorporates a Boolean operator-based crossover and mutation operator with a random mask. Here, Boolean operators (AND, OR, NOT, and XOR) are applied as a diversification strategy. A PDA simulator is implemented to validate the production rules of a CFG. The performance is evaluated against state-of-the-art algorithms. Statistical tests demonstrate the superiority of the proposed algorithm over the algorithms implemented in this paper.
https://eprints.bournemouth.ac.uk/39783/
Source: Scopus
Genetic Algorithm with Modified Reproduction Operators for Grammatical Inference
Authors: Pandey, H.
Conference: Genetic and Evolutionary Computation Conference (GECCO 2024)
Dates: 14-18 July 2024
Abstract:A grammatical inference (GI) algorithm is proposed, that utilizes a Genetic Algorithm (GA), in conjunction with a pushdown automaton (PDA) and the principle of minimum description length (MDL). GI is a methodology to infer context-free grammars (CFGs) from training data. It has wide applicability across many different fields, including natural language processing, language design, and software engineering. GAs is a search methodology that has been used in many domains and we utilize GAs as our primary search algorithm. The proposed algorithm incorporates a Boolean operator-based crossover and mutation operator with a random mask. Here, Boolean operators (AND, OR, NOT, and XOR) are applied as a diversification strategy. A PDA simulator is implemented to validate the production rules of a CFG. The performance is evaluated against state-of-the-art algorithms. Statistical tests demonstrate the superiority of the proposed algorithm over the algorithms implemented in this paper.
https://eprints.bournemouth.ac.uk/39783/
Source: Manual
Genetic algorithm with modified reproduction operators for grammatical inference
Authors: Pandey, H.
Conference: Genetic and Evolutionary Computation Conference (GECCO 2024)
Abstract:A grammatical inference (GI) algorithm is proposed, that utilizes a Genetic Algorithm (GA), in conjunction with a pushdown automaton (PDA) and the principle of minimum description length (MDL). GI is a methodology to infer context-free grammars (CFGs) from training data. It has wide applicability across many different fields, including natural language processing, language design, and software engineering. GAs is a search methodology that has been used in many domains and we utilize GAs as our primary search algorithm. The proposed algorithm incorporates a Boolean operator-based crossover and mutation operator with a random mask. Here, Boolean operators (AND, OR, NOT, and XOR) are applied as a diversification strategy. A PDA simulator is implemented to validate the production rules of a CFG. The performance is evaluated against state-of-the-art algorithms. Statistical tests demonstrate the superiority of the proposed algorithm over the algorithms implemented in this paper.
https://eprints.bournemouth.ac.uk/39783/
https://gecco-2024.sigevo.org/HomePage
Source: BURO EPrints