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Genetic algorithm with modified reproduction operators for grammatical inference.

Pandey, H., 2024. Genetic algorithm with modified reproduction operators for grammatical inference. In: Genetic and Evolutionary Computation Conference (GECCO 2024), 14-18 July 2024, Melbourne, Austrllia. (In Press)

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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.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:Context Free Grammar; Evolutionary Computation; Genetic Algorithm; Grammar Inference; Minimum Description Length Principle
Group:Faculty of Science & Technology
ID Code:39783
Deposited By: Symplectic RT2
Deposited On:09 May 2024 14:12
Last Modified:09 May 2024 14:13


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