אחיה אליסף

In Genetic Algorithms (GAs), the selection operator plays a critical role in balancing exploration and exploitation. However, classical and adaptive selection mechanisms largely rely on static rules or handcrafted heuristics that fail to adapt to the real-time dynamics of the evolving population. In this work, we introduce Deep Tournament Selection (DTS), a novel domain-independent selection operator that reformulates tournament selection as a Markov Decision Process optimized via reinforcement learning. DTS evaluates candidate solutions in a tournament using a Transformer encoder augmented with global and local rank-based positional encodings, along with a self-attention pointer mechanism. This architecture allows the selection policy to jointly capture global fitness relationships and local competitive context. The policy is trained fully online using policy-gradient reinforcement learning without requiring additional fitness evaluations, enabling the operator to dynamically adjust its selection pressure. We evaluate DTS on three canonical combinatorial optimization domains: Graph Coloring, Set Cover, and the Traveling Salesman Problem. Empirical results demonstrate that DTS achieves faster convergence, improved solution quality, and robust performance across problem instances compared to classical and dynamic selection baselines, while introducing negligible computational overhead and preserving population diversity.