Evolutionary programming based on non-uniform mutation

A new evolutionary programming using non-uniform mutation instead of Gaussian, Cauchy and Levy mutations is proposed. Evolutionary programming with non-uniform mutation (NEP) has the merits of searching the space uniformly at the early stage and very locally at the later stage during the programming. For a suite of 14 benchmark problems, NEP outperforms the improved evolutionary programming using mutation based on Levy probability distribution (ILEP) for multimodal functions with many local minima while being comparable to ILEP in performance for unimodal and multimodal functions with only a few minima. The detailed theoretical analysis of the executing process of NEP and the expected step size on non-uniform mutation are given.

[1]  L. Devroye Non-Uniform Random Variate Generation , 1986 .

[2]  Alex Fraser,et al.  Simulation of Genetic Systems by Automatic Digital Computers I. Introduction , 1957 .

[3]  Günter Rudolph,et al.  Theory of Evolutionary Algorithms: A Bird's Eye View , 1999, Theor. Comput. Sci..

[4]  Zhao Xinchao A GREEDY GENETIC ALGORITHM FOR UNCONSTRAINED GLOBAL OPTIMIZATION , 2005 .

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

[6]  Qing Zhou,et al.  Directed variation in evolution strategies , 2003, IEEE Trans. Evol. Comput..

[7]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1992, Artificial Intelligence.

[8]  Lawrence J. Fogel,et al.  Artificial Intelligence through Simulated Evolution , 1966 .

[9]  L. Darrell Whitley,et al.  Evaluating Evolutionary Algorithms , 1996, Artif. Intell..

[10]  Hans-Paul Schwefel,et al.  Numerical Optimization of Computer Models , 1982 .

[11]  Kwong-Sak Leung,et al.  Evolution strategies with exclusion-based selection operators and a Fourier series auxiliary function , 2003, Appl. Math. Comput..

[12]  Francisco Herrera,et al.  Gradual distributed real-coded genetic algorithms , 2000, IEEE Trans. Evol. Comput..

[13]  Xinghuo Yu,et al.  Conditions for the convergence of evolutionary algorithms , 2001, J. Syst. Archit..

[14]  Xin Yao,et al.  Evolutionary programming using mutations based on the Levy probability distribution , 2004, IEEE Transactions on Evolutionary Computation.

[15]  Hector J. Levesque,et al.  A New Method for Solving Hard Satisfiability Problems , 1992, AAAI.

[16]  David H. Wolpert,et al.  No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..

[17]  Thomas Bäck,et al.  Evolutionary computation: Toward a new philosophy of machine intelligence , 1997, Complex..

[18]  Xinchao Zhao,et al.  Multiple bit encoding-based search algorithms , 2005, 2005 IEEE Congress on Evolutionary Computation.

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

[20]  Coskun Hamzaçebi,et al.  A heuristic approach for finding the global minimum: Adaptive random search technique , 2006, Appl. Math. Comput..

[21]  Donald O. Walter,et al.  Self-Organizing Systems , 1987, Life Science Monographs.

[22]  David B. Fogel,et al.  Meta-evolutionary programming , 1991, [1991] Conference Record of the Twenty-Fifth Asilomar Conference on Signals, Systems & Computers.

[23]  Hao Wu,et al.  Hybrid genetic algorithm based on quantum computing for numerical optimization and parameter estimation , 2005, Appl. Math. Comput..

[24]  Anabela Simões,et al.  Transposition versus crossover: an empirical study , 1999 .

[25]  David B. Fogel,et al.  Evolutionary Computation: Towards a New Philosophy of Machine Intelligence , 1995 .

[26]  Larry J. Eshelman,et al.  The CHC Adaptive Search Algorithm: How to Have Safe Search When Engaging in Nontraditional Genetic Recombination , 1990, FOGA.

[27]  Samir W. Mahfoud Crowding and Preselection Revisited , 1992, PPSN.

[28]  Thomas Jansen,et al.  Design and Management of Complex Technical Processes and Systems by means of Computational Intelligence Methods Evolutionary Algorithms-How to Cope With Plateaus of Constant Fitness and When to Reject Strings of the Same Fitness , 2001 .

[29]  John Holland,et al.  Adaptation in Natural and Artificial Sys-tems: An Introductory Analysis with Applications to Biology , 1975 .

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

[31]  Joe Suzuki,et al.  A Markov chain analysis on simple genetic algorithms , 1995, IEEE Trans. Syst. Man Cybern..

[32]  T. Schnier,et al.  Using multiple representations in evolutionary algorithms , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

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

[34]  David B. Fogel,et al.  Evolving artificial intelligence , 1992 .

[35]  Shu-Yuen Hwang,et al.  A Genetic Algorithm with Disruptive Selection , 1993, ICGA.

[36]  Günter Rudolph,et al.  Convergence analysis of canonical genetic algorithms , 1994, IEEE Trans. Neural Networks.

[37]  Yuval Davidor,et al.  A Naturally Occurring Niche and Species Phenomenon: The Model and First Results , 1991, ICGA.

[38]  Ivanoe De Falco,et al.  Mutation-based genetic algorithm: performance evaluation , 2002, Appl. Soft Comput..

[39]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.