A self-adaptive Genetic Algorithm based on fuzzy mechanism
Xiao‐Bing Hu, Shufan Wu · 2007
This paper presents a novel self-adaptive Genetic Algorithm (GA) based on fuzzy mechanism, aiming to improve both the optimizing capability and the convergence speed. Some key factors affecting the performance of GAs are identified and analyzed, and their influences on the optimizing capability and the convergence speed are further elaborated, which are usually difficult to be described with explicit mathematical formulas. Using fuzzy mechanism, a set of fuzzy rules are used to model their complicated relationships, in order to effectively direct the online self-adaptive adjustments, such as changing the crossover and mutation probabilities, and thus to improve the optimizing capability and convergence speed. Numerical simulation tests for a typical optimization problem illustrate the advantages of the new GA against another self-adaptive GA which is based on explicit mathematical modeling of the key factors.