Genetic range genetic algorithms to obtain quasi-optimum solutions

Masao Arakawa, Tomoyuki Miyashita, Hiroshi Ishikawa

Research output: Contribution to conferencePaper

4 Citations (Scopus)

Abstract

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 only 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.

Original languageEnglish
Pages927-934
Number of pages8
Publication statusPublished - 2003 Dec 1
Event2003 ASME Design Engineering Technical Conferences and Computers and Information in Engineering Conference - Chicago, IL, United States
Duration: 2003 Sep 22003 Sep 6

Conference

Conference2003 ASME Design Engineering Technical Conferences and Computers and Information in Engineering Conference
CountryUnited States
CityChicago, IL
Period03/9/203/9/6

ASJC Scopus subject areas

  • Modelling and Simulation
  • Mechanical Engineering
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design

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  • Cite this

    Arakawa, M., Miyashita, T., & Ishikawa, H. (2003). Genetic range genetic algorithms to obtain quasi-optimum solutions. 927-934. Paper presented at 2003 ASME Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Chicago, IL, United States.