Adaptive random search with intensification and diversification combined with genetic algorithm

A novel optimization method named RasID-GA (an abbreviation of adaptive random search with intensification and diversification combined with genetic algorithm) is proposed in order to enhance the searching ability of conventional RasID, which is a kind of random search with intensification and diversification. RasID-GA is compared with conventional RasID and GA using 23 different objective functions, and it turns out that RasID-GA performs well compared with other methods.

[1]  Shingo Mabu,et al.  RasID-GA with Simplex Crossover(SPX) for Optimization problems , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[2]  Kotaro Hirasawa,et al.  A New Adaptive Random Search Method in Neural Networks , 1998 .

[3]  Aimo A. Törn,et al.  Global Optimization , 1999, Science.

[4]  Shingo Mabu,et al.  Adaptive random search with intensification and diversification combined with genetic algorithm , 2005, 2005 IEEE Congress on Evolutionary Computation.

[5]  Kotaro Hirasawa,et al.  Universal learning network and its application to chaos control , 2000, Neural Networks.

[6]  Jinglu Hu,et al.  Adaptive Random Search Approach to Identification of Neural Network Model , 2000 .

[7]  Kotaro Hirasawa,et al.  Multi-branch structure of layered neural networks , 2002, Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02..

[8]  Pei Yee Ho,et al.  Simple addition of ranking method for constrained optimization in evolutionary algorithms , 2005, GECCO '05.

[9]  Shingo Mabu,et al.  Optimization Method RasID-GA for Numerical Constrained Optimization Problems , 2007, J. Adv. Comput. Intell. Intell. Informatics.

[10]  Jing Tang,et al.  Adaptation for parallel memetic algorithm based on population entropy , 2006, GECCO '06.

[11]  Wei Yuan,et al.  A New Trust-Region Algorithm for Equality Constrained Optimization , 2002, Comput. Optim. Appl..

[12]  William E. Hart,et al.  A Convergence Analysis of Unconstrained and Bound Constrained Evolutionary Pattern Search , 2001, Evolutionary Computation.

[13]  Min Han,et al.  Support Vector Echo-State Machine for Chaotic Time-Series Prediction , 2007, IEEE Transactions on Neural Networks.

[14]  Francisco Herrera,et al.  Real-Coded Memetic Algorithms with Crossover Hill-Climbing , 2004, Evolutionary Computation.

[15]  Yuping Wang,et al.  An orthogonal genetic algorithm with quantization for global numerical optimization , 2001, IEEE Trans. Evol. Comput..

[16]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[17]  M. Yamamura,et al.  Multi-parent recombination with simplex crossover in real coded genetic algorithms , 1999 .

[18]  Zbigniew Michalewicz,et al.  Evolutionary Algorithms for Constrained Parameter Optimization Problems , 1996, Evolutionary Computation.

[19]  Yoshio Mogami,et al.  A hybrid algorithm for finding the global minimum of error function of neural networks and its applications , 1994, Neural Networks.

[20]  Isao Ono,et al.  A Real Coded Genetic Algorithm for Function Optimization Using Unimodal Normal Distributed Crossover , 1997, ICGA.

[21]  David E. Goldberg,et al.  Simplex crossover and linkage identification: single-stage evolution vs. multi-stage evolution , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[22]  D. E. Goldberg,et al.  Genetic Algorithms in Search, Optimization & Machine Learning , 1989 .

[23]  Kotaro Hirasawa,et al.  RasID - Random Search for Neural Network Training , 1998, J. Adv. Comput. Intell. Intell. Informatics.

[24]  Chuen-Tsai Sun,et al.  Neuro-fuzzy modeling and control , 1995, Proc. IEEE.

[25]  H. Khaloozadeh,et al.  Predicting the Mackey Glass Chaotic Time Series Using Genetic Algorithm , 2006, 2006 SICE-ICASE International Joint Conference.

[26]  Hans-Paul Schwefel,et al.  Evolution and optimum seeking , 1995, Sixth-generation computer technology series.

[27]  James E. Baker,et al.  Adaptive Selection Methods for Genetic Algorithms , 1985, International Conference on Genetic Algorithms.

[28]  Shingo Mabu,et al.  Training of Multi-Branch Neural Networks using RasID-GA , 2007, 2007 IEEE Congress on Evolutionary Computation.

[29]  Gilbert Syswerda,et al.  Uniform Crossover in Genetic Algorithms , 1989, ICGA.

[30]  Kotaro Hirasawa,et al.  Enhancement of RasID and Its Evaluation , 2002 .

[31]  Kalyanmoy Deb,et al.  Self-Adaptive Genetic Algorithms with Simulated Binary Crossover , 2001, Evolutionary Computation.

[32]  N. Baba.  A hybrid algorithm for finding a global minimum , 1983 .

[33]  S. Mabu,et al.  Constrained Global Optimization Problems by RasID-GA , 2006, 2006 SICE-ICASE International Joint Conference.

[34]  Roger J.-B. Wets,et al.  Minimization by Random Search Techniques , 1981, Math. Oper. Res..

[35]  Xin Yao,et al.  Stochastic ranking for constrained evolutionary optimization , 2000, IEEE Trans. Evol. Comput..

[36]  Kumar Chellapilla,et al.  Combining mutation operators in evolutionary programming , 1998, IEEE Trans. Evol. Comput..

[37]  N. Baba,et al.  An intelligent forecasting system of stock price using neural networks , 1992, [Proceedings 1992] IJCNN International Joint Conference on Neural Networks.

[38]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[39]  Michel Verleysen,et al.  Non-linear financial time series forecasting application to the Bel 20 stock market index , 2000 .

[40]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[41]  Kotaro Hirasawa,et al.  Universal Learning Network Theory , 1996 .

[42]  Xin Yao,et al.  Fast Evolution Strategies , 1997, Evolutionary Programming.

[43]  K. G. Srinivasa,et al.  An efficient fuzzy based neuro - genetic algorithm for stock market prediction , 2006, Int. J. Hybrid Intell. Syst..

[44]  Norio Baba,et al.  A new approach for finding the global minimum of error function of neural networks , 1989, Neural Networks.

[45]  A. A. Torn.  Cluster Analysis Using Seed Points and Density-Determined Hyperspheres as an Aid to Global Optimization , 1977, IEEE Transactions on Systems, Man, and Cybernetics.

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

[47]  P. Werbos,et al.  Beyond Regression : "New Tools for Prediction and Analysis in the Behavioral Sciences , 1974 .

[48]  Norio Baba,et al.  Utilization of soft computing techniques for constructing reliable decision support systems for dealing stocks , 2002, Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290).

[49]  Kotaro Hirasawa,et al.  Multi-branch structure and its localized property in layered neural networks , 2004 .

[50]  Xin Yao,et al.  Evolutionary programming made faster , 1999, IEEE Trans. Evol. Comput..