Hybrid AI Optimization Integrating Genetic Algorithms and Neural Networks

Abstract

Hybrid AI optimization methods that integrate Genetic Algorithms (GAs) and Neural Networks (NNs) have garnered significant attention for their potential to solve complex optimization problems. This research paper explores the synergy between GAs and NNs, highlighting their complementary strengths. We present a comprehensive review of the state-of-the-art hybrid methodologies, analyze their performance in various application domains, and propose a novel framework for enhanced optimization. Experimental results demonstrate the efficacy of the proposed hybrid approach in terms of convergence speed, solution quality, and robustness.