אחיה אליסף

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. To overcome this, we propose a novel data augmentation strategy that generates pseudo-training examples derived from the population’s elite. This strategy enriches the training signal by constructing augmented trajectories toward high-fitness solutions, stabilizing the reinforcement learning process. We evaluate the operator across four diverse domains: Frozen Lake, Artificial Ant, Graph Coloring, and Unweighted Set Cover. Our results demonstrate that BERT Mutation consistently outperforms traditional stochastic operators (One-Point, Uniform, and Zigzag) and an adaptive operator (AOS) in both convergence speed and final solution quality. Notably, BERT Mutation achieves superior average fitness in approximately half the wall-clock time and fewer than half the generations required by competing baselines. Furthermore, diversity analysis confirms that BERT Mutation preserves structured, meaningful population diversity, avoiding the premature convergence seen in simpler operators.