In some cases of developing a new product, response surface of an objective function is not always single peaked function, and it is often multi-peaked function. In that case, designers would like to have not oniy global optimum solution but also as many local optimum solutions and/or quasi-optimum solutions as possible, so that he or she can select one out of them considering the other conditions that are not taken into account priori to optimization. Although this information is quite useful, it is not that easy to obtain with a single trial of optimization. In this study, we will propose a screening of fitness function in genetic algorithms (GA). Which change fitness function during searching. Therefore, GA needs to have higher flexibility in searching. Genetic Range Genetic Algorithms include a number of searching range in a single generation. Just like there are a number of species in wild life. Therefore, it can arrange to have both global searching range and also local searching range with different fitness function. In this paper, we demonstrate the effectiveness of the proposed method through a simple benchmark test problems.
- Design Engineering Division and Computers and Information in Engineering Division
Genetic Range Genetic Algorithms to Obtain Quasi-Optimum Solutions
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Arakawa, M, Miyashita, T, & Ishikawa, H. "Genetic Range Genetic Algorithms to Obtain Quasi-Optimum Solutions." Proceedings of the ASME 2003 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Volume 2: 29th Design Automation Conference, Parts A and B. Chicago, Illinois, USA. September 2–6, 2003. pp. 927-934. ASME. https://doi.org/10.1115/DETC2003/DAC-48800
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