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

Abstract: This paper introduces an enhanced Genetic Algorithm technique, which optimizes neural networks for binary image classificatio n 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, while the Follower promotes exploration by preserving diversity and avoiding premature convergence. 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 architect ure configurations (5 for each set), ranked by Pareto dominance and cost post - optimization; and (3) achieved better results compared to gradient - based methods. Experimental results show that the proposed method achieves 99.04% training acc uracy and 80% testing accuracy (cost = 0. 06) on a three - layer network with architecture [12288, 17, 4, 1], higher performance 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]. Reinforcement Learning (RL) is the strategy of learning where an agent learns optimal behaviors by interacting with an environment through trial and error. The agent performs actions, receives rewards or penalties as feedback, and aims to maximize the cumulative reward over time [1] . RL has made exciting progress in domains like game playing (e.g., AlphaGo), robotics, and autonomous systems. However, it still faces challenges, such as sparse rewards [2,3], high - dimensional action spaces [4], and training instability [5] . Genetic Algorithms (GA), inspired by the principles of natural evolution, such as selection, mutation, and reproduction, offer versatile support for RL across multiple stages [6] .