An Adaptive Parameters Binary-Real Coded Genetic Algorithm for Real Parameter Optimization: Performance Analysis and Estimation of Optimal Control Parameters
Genetic algorithms (GAs) are vital members within the family biologically inspired algorithms. It has been proven that the performance of GAs is largely affected by the type of encoding schemes used to encode optimization problems. Binary and real encoding schemes are the most popular ones. However, it is still controversial to decide the superiority of one of them for GAs performance. Therefore, we have recently proposed binary-real coded GA (BRGA) that has the ability to use both encoding schemes at the same time. BRGA relays on a parameterized hybrid scheme to share the computational power and coordinate the cooperation between binary coded GA (BGA) and real coded GA (RGA). In this article, we use CEC2005 benchmark suite of 25 functions to analyze quality and time performance of BRGA and in comparison with original binary and real coded component GAs. To demonstrate the performance of BRGA, we compare it with the performance of some other EAs from the literature. In addition, we implement a robust parameter tuning procedure that relies on techniques from statistical testing, design of experiments and Response Surface Methodology (RSM) to estimate the optimal values for control parameters that can secure a good performance for BRGA against specific problems at hand.
Keywords: Binary coded GA(BGA), Real coded GA(RGA), Hybrid Scheme, Design of Experiments.
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ABOUT THE AUTHORS
Omar Abdul-Rahman
received the B.Sc. and the M.Sc. from the Department of Electrical & Electronic Engineering, University of Technology, Baghdad, Iraq in 2004 and 2006 respectively. Currently, he is pursuing PhD degree in Information Technology, Hokkaido University under Japanese government scholarship (MEXT). His current research interests include genetic algorithms, evolutionary computation, multiobjective optimization and cloud computing.
Masaharu Munetomo
received the PhD in information engineering from graduate school of engineering, Hokkaido University in 1996. From 1998 to 1999, he joined Illinois Genetic Algorithms Laboratory (IlliGAL), university of Illinois at Urbana-Champaign as a visiting scholar. Since 1999, he works for Hokkaido University as an associate professor. He also engaged in designing Hokkaido university academic cloud at information initiative center of the university.
Kiyoshi Akama
Kiyoshi Akama received the PhD in control engineering from Tokyo Institute of Technology in 1989. Now he works as a professor at Information Systems Design Laboratory, Division of Large Scale Computing Systems, Information Initiative Center, Hokkaido University, Japan. His research interests include automatic program construction, logical problem solving, equivalent transformation computation model, and artificial intelligence.
Omar Abdul-Rahman
received the B.Sc. and the M.Sc. from the Department of Electrical & Electronic Engineering, University of Technology, Baghdad, Iraq in 2004 and 2006 respectively. Currently, he is pursuing PhD degree in Information Technology, Hokkaido University under Japanese government scholarship (MEXT). His current research interests include genetic algorithms, evolutionary computation, multiobjective optimization and cloud computing.
Masaharu Munetomo
received the PhD in information engineering from graduate school of engineering, Hokkaido University in 1996. From 1998 to 1999, he joined Illinois Genetic Algorithms Laboratory (IlliGAL), university of Illinois at Urbana-Champaign as a visiting scholar. Since 1999, he works for Hokkaido University as an associate professor. He also engaged in designing Hokkaido university academic cloud at information initiative center of the university.
Kiyoshi Akama
Kiyoshi Akama received the PhD in control engineering from Tokyo Institute of Technology in 1989. Now he works as a professor at Information Systems Design Laboratory, Division of Large Scale Computing Systems, Information Initiative Center, Hokkaido University, Japan. His research interests include automatic program construction, logical problem solving, equivalent transformation computation model, and artificial intelligence.
