抄録
Offer Organization: Japan Society for the Promotion of Science, System Name: Grants-in-Aid for Scientific Research, Category: Grant-in-Aid for Scientific Research (C), Fund Type: competitive_research_funding, Overall Grant Amount: - (direct: 3300000, indirect: 990000)
We studied evolutionary computation methodology to optimize multiple objective functions simultaneously. For continuous problems with real value design variables and discrete problems with discrete design variables, binary 0/1 especially, we respectively proposed test optimization problems that could specify similarities among objective functions. For these problems, we proposed evolutionary optimization algorithms that estimate similarities among objective functions by solution distributions in the variable space and utilize them to enhance or suppress the cooperative search in crossover generating new solutions by recombining two existing solutions. Results showed that the proposed algorithms could estimate similarities among multiple objective functions and utilized them to improve the simultaneous optimization of multiple objective functions compared to conventional evolutionary algorithms.