Mastering Genetic Algorithms: A Forest-Based Approach for Function Optimization.

Abstract

The persistent challenge in evolutionary computation has been the optimization of genetic algorithms. This research proposes a new approach that uses a master-slave framework to coordinate a swarm of genetic algorithms. The novel methodology presented here offers a paradigm shift in how evolutionary optimization is done, where the orchestration of multiple GAs occurs through each GA acting as an individual agent under the direction of a central master algorithm. This synergy between agents enables one to optimize selection strategies with a boost in efficiency and effectiveness regarding the maximization of the fitness functions across complex solution spaces. This work mainly focuses on function optimization as well as classification problems when evaluating the effectiveness of this proposed architecture under very strenuous testing. The results have shown robustness and scalability with a wide range of problems. The MSGA framework achieved accuracy levels up to 99.62% for function optimization and significantly outperformed traditional single GA methods. The best results were achieved using a combination of single-point crossover, random resetting mutation, and roulette wheel selection strategies, underscoring the impact of diverse genetic operations within the ensemble, population of dimension (10,10) and values ranges from -10 to +10 are randomly generated for a test drive. The interplay of multiple GAs with a central governing intelligence is poised to revolutionize the optimization process. Outcomes show how distributed intelligence coupled with centralized coordination can considerably enhance the capacity of Genetic Algorithms to solve real-world problems and advance function optimization and classification performance to unprecedented levels. This work represents a significant step toward the future of evolutionary optimization where Sequential GAs and adaptive strategy adjustment fuse to make convergence faster, results more accurate, and adaptability superior