Dual-Individual Genetic Algorithm: A Dual-Individual Approach for Efficient Training of Multi-Layer Neural Networks

This paper introduces an enhanced Genetic Algorithm technique called Dual-Individual Genetic Algorithm (Dual-Individual GA), which optimizes neural networks for binary image classification tasks, such as cat vs. non-cat classification. The proposed method employs only two individuals for crossover, represented by two parameter sets: Leader and Follower. The Leader focuses on exploitation, representing the primary optimal solution at even-indexed positions (0, 2, 4, ...), while the Follower promotes exploration by preserving diversity and avoiding premature convergence, operating at odd-indexed positions (1, 3, 5, ...). Leader and Follower are modeled as two phases or roles. The key contributions of this work are threefold: (1) a self-adaptive layer dimension mechanism that eliminates the need for manual tuning of layer architectures; (2) generates two parameter sets, leader and follower parameter sets, with 10 layer architecture configurations (5 for each set), ranked by Pareto dominance and cost. post-optimization; and (3) demonstrated superior performance compared to traditional gradient-based methods. Experimental results show that the Dual-Individual GA achieves 99.04% training accuracy and 80% testing accuracy (cost = 0.034) on a three-layer network with architecture [12288, 17, 4, 1], outperforming a gradient-based approach that achieves 98% training accuracy and 80% testing accuracy (cost = 0.092) on a four-layer network with architecture [12288, 20, 7, 5, 1]. These findings highlight the efficiency and effectiveness of the proposed method in optimizing neural networks.