ACHIYA ELYASAF

Traditional genetic algorithm mutation strategies typically apply stochastic gene modifications without considering broader genomic context or learning from historical evolutionary data. To address this, we introduce BERT Mutation for Genetic Algorithms (GAs), a novel, domain-independent contextual mutation operator that adapts the Transformer architecture to fixed-length genetic representations. While deep learning-based operators have shown success in Genetic Programming, extending them to GAs is challenged by ``semantic opacity''---the lack of explicit structural definitions in linear integer or binary vectors.