Fuzzy Guiding of Roulette Selection in Evolutionary Algorithms

This paper presents, discusses, and tests a novel method for guiding roulette selection in evolutionary algorithms. The new method uses fuzzy logic and incorporates information from both current and historical generations to predict the best scheme for the selection process. Fuzzy logic controls the probability of selecting individuals to the parent pool, based on historical data from the evolution process and the relationship between an individual’s fitness and the average fitness of the population. The new algorithm outperforms existing solutions by ensuring a proper balance between exploring new regions of the search space and exploiting previously found ones. The proposed system enhances the performance, efficiency, and robustness of evolutionary algorithms while reducing the risk of stagnation in suboptimal solutions. Results of experiments demonstrate that the newly developed algorithm is more efficient and resistant to premature convergence than standard evolutionary algorithms. Tests on both function optimization problems and real-world connected facility localization problems confirm the robustness of the newly developed algorithm. The algorithm can be an effective tool in solving a wide range of optimization problems, for example, optimization of computer network infrastructure.