Abstract
Genetic Algorithm is an approximate optimizing technipue imitating the evolution of biological life.
GA includes some problems because it is a search method contained the stochastic factors.
For example, there is a problem that the evolution of population remains stagnant at the initial and the end stage of search because the values of genetic parameters such as the crossover rate, the mutation rate and so on are constant. To solve this problem, in our laboratory we have already proposed Fuzzy Adaptive Search method for Parallel Genetic Algorithms (FASPGA) that tunes the genetic parameters appropriately according to the search stage by the fuzzy rule to improve the efficiency of the search. In this research,
we propose a method with high search efficiency for both the single-peak and the multi-peak solution problem by incorporating Artificial Bee Colony Algorithm
in FASPGA. We also report the result for the function optimization simulation performed to confirm the efficiency of our method.
