Valley-Adaptive Clearing Scheme for Multimodal Optimization Evolutionary Search

Recent studies [13, 18] have shown that clearing schemes are efficient multi-modal optimization methods. They efficiently reduce genetic drift which is the direct reason for premature convergence in genetic algorithms. However, clearing schemes assumed a landscape containing equal-spaced basins when using a fixed niche radius. Further, most clearing methods employ policies that favor elitists, thus affecting the explorative capabilities of the search. In this paper, we present a valley adaptive clearing scheme, aiming at adapting to non-uniform width of the valleys in the problem landscape. The framework of the algorithm involves hill-valley initialization, valley-adaptive clearing and archiving. Experimental results on benchmark functions are presented to demonstrate that the proposed scheme uncovers more local optima solutions and displays excellent robustness to varying niche radius than other clearing compeers.

[1]  Alain Pétrowski,et al.  A clearing procedure as a niching method for genetic algorithms , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[2]  Xin Yao,et al.  Empirical analysis of evolutionary algorithms with immigrants schemes for dynamic optimization , 2009, Memetic Comput..

[3]  David E. Goldberg,et al.  Probabilistic Crowding: Deterministic Crowding with Probabilistic Replacement , 1999 .

[4]  Daniela Zaharie.  A MULTIPOPULATION DIFFERENTIAL EVOLUTION ALGORITHM FOR MULTIMODAL OPTIMIZATION , 2004 .

[5]  K. Warwick,et al.  Dynamic Niche Clustering: a fuzzy variable radius niching technique for multimodal optimisation in GAs , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[6]  Xiaodong Yin,et al.  A Fast Genetic Algorithm with Sharing Scheme Using Cluster Analysis Methods in Multimodal Function Optimization , 1993 .

[7]  Edmund K. Burke,et al.  Improving the scalability of rule-based evolutionary learning , 2009, Memetic Comput..

[8]  Yueguang Lu,et al.  A direct first principles study on the structure and electronic properties of BexZn1−xO , 2007 .

[9]  David E. Goldberg,et al.  Genetic Algorithms with Sharing for Multimodalfunction Optimization , 1987, ICGA.

[10]  Zexuan Zhu,et al.  Markov blanket-embedded genetic algorithm for gene selection , 2007, Pattern Recognit..

[11]  Ruhul A. Sarker,et al.  Memetic algorithms for solving job-shop scheduling problems , 2009, Memetic Comput..

[12]  Ferrante Neri,et al.  An Adaptive Multimeme Algorithm for Designing HIV Multidrug Therapies , 2007, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[13]  Harold Soh,et al.  Multiscale approach to explore the potential energy surface of water clusters (H2O)nn , 2008, The journal of physical chemistry. A.

[14]  Dumitru Dumitrescu,et al.  A new evolutionary model for detecting multiple optima , 2007, GECCO '07.

[15]  Kai-Yew Lum,et al.  Max-min surrogate-assisted evolutionary algorithm for robust design , 2006, IEEE Transactions on Evolutionary Computation.

[16]  Georges R. Harik,et al.  Finding Multimodal Solutions Using Restricted Tournament Selection , 1995, ICGA.

[17]  Xin Yao,et al.  Evolutionary Optimization , 2002 .

[18]  Bu-Sung Lee,et al.  Memetic algorithm using multi-surrogates for computationally expensive optimization problems , 2007, Soft Comput..

[19]  A. Keane,et al.  Evolutionary Optimization of Computationally Expensive Problems via Surrogate Modeling , 2003 .

[20]  Jürgen Branke,et al.  Evolutionary Optimization in Dynamic Environments , 2001, Genetic Algorithms and Evolutionary Computation.

[21]  Kalyanmoy Deb,et al.  Comparison of multi-modal optimization algorithms based on evolutionary algorithms , 2006, GECCO.

[22]  Yew-Soon Ong,et al.  Hybrid evolutionary algorithm with Hermite radial basis function interpolants for computationally expensive adjoint solvers , 2008, Comput. Optim. Appl..