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 relies 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 CEC’2005 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.

[1]  R. Salomon.  Re-evaluating genetic algorithm performance under coordinate rotation of benchmark functions. A survey of some theoretical and practical aspects of genetic algorithms. , 1996, Bio Systems.

[2]  Asim Munawar,et al.  Solving Extremely Difficult MINLP Problems Using Adaptive Resolution Micro-GA with Tabu Search , 2011, LION.

[3]  Thomas Bartz-Beielstein,et al.  Experimental Research in Evolutionary Computation - The New Experimentalism , 2010, Natural Computing Series.

[4]  Hiroaki Satoh,et al.  Minimal generation gap model for GAs considering both exploration and exploitation , 1996 .

[5]  Masaharu Munetomo,et al.  Multi-Level Autonomic Architecture for the Management of Virtualized Application Environments in Cloud Platforms , 2011, 2011 IEEE 4th International Conference on Cloud Computing.

[6]  Rajarshi Das,et al.  A Study of Control Parameters Affecting Online Performance of Genetic Algorithms for Function Optimization , 1989, ICGA.

[7]  Helder Coelho,et al.  The Design of Innovation: Lessons from and for Competent Genetic Algorithms by David E. Goldberg , 2005, J. Artif. Soc. Soc. Simul..

[8]  Petr Posík,et al.  Real-parameter optimization using the mutation step co-evolution , 2005, 2005 IEEE Congress on Evolutionary Computation.

[9]  Jing J. Liang,et al.  Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization , 2005 .

[10]  Francisco Herrera,et al.  Adaptive local search parameters for real-coded memetic algorithms , 2005, 2005 IEEE Congress on Evolutionary Computation.

[11]  Edwin R. Hancock,et al.  Empirical Modelling of Genetic Algorithms , 2001, Evolutionary Computation.

[12]  Daniel Manrique,et al.  Cooperative binary-real coded genetic algorithms for generating and adapting artificial neural networks , 2003, Neural Computing & Applications.

[13]  P. Preux,et al.  Towards hybrid evolutionary algorithms , 1999 .

[14]  Masaharu Munetomo,et al.  An adaptive resolution hybrid binary-real coded genetic algorithm , 2011, Artificial Life and Robotics.

[15]  E. Herrera‐Viedma,et al.  Fuzzy Tools to Improve Genetic Algorithms Fuzzy Tools to Improve Genetic Algorithms 1 , 1994 .

[16]  Pedro J. Ballester,et al.  Real-parameter optimization performance study on the CEC-2005 benchmark with SPC-PNX , 2005, 2005 IEEE Congress on Evolutionary Computation.

[17]  M. Balazinski,et al.  Real/Binary-Like Coded Genetic Algorithm to Automatically Generate Fuzzy Knowledge Bases , 2003, 2003 4th International Conference on Control and Automation Proceedings.

[18]  K. E. Hillstrom,et al.  A Simulation Test Approach to the Evaluation of Nonlinear Optimization Algorithms , 1977, TOMS.

[19]  M. Arakawa,et al.  Development of Adaptive Real Range(ARRange)Genetic Algorithms , 1998 .

[20]  Kalyanmoy Deb,et al.  Simulated Binary Crossover for Continuous Search Space , 1995, Complex Syst..

[21]  Shigenobu Kobayashi,et al.  A Real-Coded Genetic Algorithm for Function Optimization Using the Unimodal Normal Distribution Crossover , 1999 .

[22]  Marcus Gallagher,et al.  Experimental results for the special session on real-parameter optimization at CEC 2005: a simple, continuous EDA , 2005, 2005 IEEE Congress on Evolutionary Computation.

[23]  Margaret J. Robertson,et al.  Design and Analysis of Experiments , 2006, Handbook of statistics.

[24]  Chang Wook Ahn,et al.  On the practical genetic algorithms , 2005, GECCO '05.

[25]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[26]  Jiju Antony,et al.  Design of experiments for engineers and scientists , 2003 .

[27]  K Krishnakumar,et al.  Solving large parameter optimization problems using genetic algorithms , 1995 .