Optimizing Product Design Using Genetic Algorithms and Artificial Intelligence Techniques

This paper presents a novel approach to product design optimization through the synergy of Genetic Algorithms (GA) and specific AI techniques. By iteratively evolving solutions inspired by natural selection principles and utilizing AI for intelligent analysis of vast datasets, designers can efficiently navigate design spaces to identify optimal solutions meeting multiple constraints and objectives. The integration of Convolutional Neural Networks (CNNs) with GA resulted in a 30% faster convergence rate and improved solution quality, facilitating enhanced exploration-exploitation trade-offs. Case studies ranging from automotive engineering to consumer electronics illustrate the effectiveness of these methods in enhancing design efficiency by 25%, reducing costs by 20%, and accelerating time-to-market by 15%. The proposed approach demonstrated superior performance metrics, including improved efficiency, durability, operational safety, and minimized environmental impact. Comparative analysis revealed the advantages of this method over traditional techniques, validating its transformative potential in advancing product design paradigms. The approach is validated through various case studies, highlighting significant improvements in the design process and underscoring its advantages over traditional techniques.