USING CHI-SQUARE MATRIX TO STRENGTHEN MULTI-OBJECTIVE EVOLUTIONARY ALGORITHM

Many complex engineering problems have multi-objectives where each objective is conflicting with others. However, a lot research Jiradej Ponsawat et al. 2 works in optimization by Competent Genetic Algorithm are focused on single objective methods. These algorithms work very well for single objective problems but stumble when trying to discover a large number of solutions naturally occurred in multi-objective problems. There are many multi-objective problems where solutions share common characteristic, for example decomposable multi-objective problems. This characteristic can be exploited to identify and compose these common structures. This work proposes to apply the concept of Building Blocks to improve evolutionary algorithms to tackle such problems. Building Block Identification algorithm is used to guide the crossover operator in order to maintain good Building Blocks and mix them effectively. The proposed method is evaluated by using Building Block Identification guided crossover in a well-known Genetic Algorithm to solve multiple-objective problems. The result shows that the proposed method is effective. Moreover, it obtains a good spread of solutions even when the Building Blocks are loosely encoded.

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

[2]  Kalyanmoy Deb,et al.  Massive Multimodality, Deception, and Genetic Algorithms , 1992, PPSN.

[3]  Kalyanmoy Deb,et al.  Analyzing Deception in Trap Functions , 1992, FOGA.

[4]  Kalyanmoy Deb,et al.  Multimodal Deceptive Functions , 1993, Complex Syst..

[5]  Kalyanmoy Deb,et al.  RapidAccurate Optimization of Difficult Problems Using Fast Messy Genetic Algorithms , 1993, ICGA.

[6]  Kalyanmoy Deb,et al.  Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms , 1994, Evolutionary Computation.

[7]  David E. Goldberg,et al.  Learning Linkage , 1996, FOGA.

[8]  Hillol Kargupta,et al.  The Gene Expression Messy Genetic Algorithm , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[9]  D. Goldberg,et al.  BOA: the Bayesian optimization algorithm , 1999 .

[10]  Eckart Zitzler,et al.  Evolutionary algorithms for multiobjective optimization: methods and applications , 1999 .

[11]  D. Goldberg,et al.  Escaping hierarchical traps with competent genetic algorithms , 2001 .

[12]  M. Clergue,et al.  GA-hard functions built by combination of Trap functions , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[13]  David E. Goldberg,et al.  The Design of Innovation: Lessons from and for Competent Genetic Algorithms , 2002 .

[14]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[15]  Gary B. Lamont,et al.  Evolutionary Algorithms for Solving Multi-Objective Problems , 2002, Genetic Algorithms and Evolutionary Computation.

[16]  David E. Goldberg,et al.  Multi-objective bayesian optimization algorithm , 2002 .

[17]  Marco Laumanns,et al.  Bayesian Optimization Algorithms for Multi-objective Optimization , 2002, PPSN.

[18]  Prabhas Chongstitvatana,et al.  Building-block Identification by Simultaneity Matrix , 2003, Soft Comput..

[19]  Thomas Bäck,et al.  An analysis of the behavior of simplified evolutionary algorithms on trap functions , 2003, IEEE Trans. Evol. Comput..

[20]  Prabhas Chongstitvatana,et al.  Simultaneity Matrix for Solving Hierarchically Decomposable Functions , 2004, GECCO.

[21]  Prabhas Chongstitvatana,et al.  Chi-Square Matrix: An Approach for Building-Block Identification , 2004, ASIAN.

[22]  Khoa Duc Tran,et al.  Elitist non-dominated sorting GA-II (NSGA-II) as a parameter-less multi-objective genetic algorithm , 2005, Proceedings. IEEE SoutheastCon, 2005..

[23]  David E. Goldberg,et al.  Limits of scalability of multiobjective estimation of distribution algorithms , 2005, 2005 IEEE Congress on Evolutionary Computation.

[24]  Gary B. Lamont,et al.  An effective explicit building block MOEA, the MOMGA-IIa , 2005, 2005 IEEE Congress on Evolutionary Computation.

[25]  Prabhas Chongstitvatana,et al.  A quantitative approach for validating the building-block hypothesis , 2005, 2005 IEEE Congress on Evolutionary Computation.

[26]  Cláudio F. Lima,et al.  Revisiting evolutionary algorithms with on-the-fly population size adjustment , 2006, GECCO '06.

[27]  Masaharu Munetomo,et al.  Linkage Analysis in Genetic Algorithms , 2008, Computational Intelligence Paradigms.

[28]  Zoran Kapelan,et al.  Probabilistic building block identification for the optimal design and rehabilitation of water distribution systems , 2009 .

[29]  K. Zdravko,et al.  Multiobjective Design of Wireless Ad Hoc Networks: Security, Real-Time and Lifetime , 2023, Journal of Telecommunications and Information Technology.

[30]  Prabhas Chongstitvatana,et al.  Real options approach to evaluating genetic algorithms , 2009, Appl. Soft Comput..