Performance Improvement of Hybrid Real-Coded Genetic Algorithm with Local Search and Its Applications

We have already proposed a hybrid real-coded genetic algorithm with local search (HRGA/LS) for improving the search performance of a real-coded genetic algorithm. To further improve the search performance of HRGA/LS, this paper proposes to use the blend crossover, BLX-alpha, instead of simple crossover. It is expected to find still better solutions by increasing the diversity of generated individuals. Simulation experiments elucidate the characteristics of group search of HRGA/LS with BLX-alpha, and demonstrate that the proposed method vastly improves search performance

[1]  Xin Yao,et al.  A new evolutionary system for evolving artificial neural networks , 1997, IEEE Trans. Neural Networks.

[2]  Adam Prügel-Bennett,et al.  When a genetic algorithm outperforms hill-climbing , 2004, Theor. Comput. Sci..

[3]  Brian W. Kernighan,et al.  An Effective Heuristic Algorithm for the Traveling-Salesman Problem , 1973, Oper. Res..

[4]  R. Darnell.  Translation , 1873, The Indian medical gazette.

[5]  Sang-Kyung Lee,et al.  Translation, rotation and scale invariant pattern recognition using spectral analysis and hybrid genetic-neural-fuzzy networks , 1996 .

[6]  Philipp Slusallek,et al.  Introduction to real-time ray tracing , 2005, SIGGRAPH Courses.

[7]  Francisco Herrera,et al.  Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis , 1998, Artificial Intelligence Review.

[8]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[9]  Kalyanmoy Deb,et al.  Real-coded Genetic Algorithms with Simulated Binary Crossover: Studies on Multimodal and Multiobjective Problems , 1995, Complex Syst..

[10]  David E. Goldberg,et al.  Real-coded Genetic Algorithms, Virtual Alphabets, and Blocking , 1991, Complex Syst..

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

[12]  Shigeyoshi Tsutsui,et al.  A study on the effect of multi-parent recombination in real coded genetic algorithms , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[13]  Sam Kwong,et al.  Genetic Algorithms in Filtering , 1999 .

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

[15]  Shigeyoshi Tsutsui,et al.  Multi-parent Recombination in Genetic Algorithms with Search Space Boundary Extension by Mirroring , 1998, PPSN.

[16]  Hong Zhang,et al.  A solution to combinatorial optimization with time-varying parameters by a hybrid genetic algorithm , 2004 .

[17]  I. Ono,et al.  A Genetic Algorithm with Characteristic Preservation for Function Optimization , 1996 .

[18]  Nicholas J. Radcliffe,et al.  Equivalence Class Analysis of Genetic Algorithms , 1991, Complex Syst..

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