Choosing selection pressure for wide-gap problems

Abstract To exploit an evolutionary algorithm’s performance to the full extent, the selection scheme should be chosen carefully. Empirically, it is commonly acknowledged that low selection pressure can prevent an evolutionary algorithm from premature convergence, and is thereby more suitable for wide-gap problems. However, there are few theoretical time complexity studies that actually give the conditions under which a high or a low selection pressure is better. In this paper, we provide a rigorous time complexity analysis showing that low selection pressure is better for the wide-gap problems with two optima.

[1]  Shane Legg,et al.  Fitness uniform optimization , 2006, IEEE Transactions on Evolutionary Computation.

[2]  L. Darrell Whitley,et al.  The GENITOR Algorithm and Selection Pressure: Why Rank-Based Allocation of Reproductive Trials is Best , 1989, ICGA.

[3]  Yang Yu,et al.  A new approach to estimating the expected first hitting time of evolutionary algorithms , 2006, Artif. Intell..

[4]  Xin Yao,et al.  Towards an analytic framework for analysing the computation time of evolutionary algorithms , 2003, Artif. Intell..

[5]  R. Syski Passage Times for Markov Chains , 1992 .

[6]  Thomas Jansen,et al.  On the analysis of the (1+1) evolutionary algorithm , 2002, Theor. Comput. Sci..

[7]  Thomas Bäck,et al.  An evolutionary approach to combinatorial optimization problems , 1994, CSC '94.

[8]  Franz Rothlauf,et al.  On the importance of the second largest eigenvalue on the convergence rate of genetic algorithms , 2001 .

[9]  The Centre of Excellence for Research in Computational Intelligence and Applications , .

[10]  Marc Schoenauer,et al.  Rigorous Hitting Times for Binary Mutations , 1999, Evolutionary Computation.

[11]  Adam Prügel-Bennett,et al.  Genetic drift in genetic algorithm selection schemes , 1999, IEEE Trans. Evol. Comput..

[12]  Kalyanmoy Deb,et al.  A Comparative Analysis of Selection Schemes Used in Genetic Algorithms , 1990, FOGA.

[13]  Lothar Thiele,et al.  A Comparison of Selection Schemes used in Genetic Algorithms , 1995 .

[14]  Kenneth A. De Jong,et al.  Design and Management of Complex Technical Processes and Systems by Means of Computational Intelligence Methods on the Choice of the Offspring Population Size in Evolutionary Algorithms on the Choice of the Offspring Population Size in Evolutionary Algorithms , 2004 .

[15]  Xin Yao,et al.  A New Approach for Analyzing Average Time Complexity of Population-Based Evolutionary Algorithms on Unimodal Problems , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[16]  Lishan Kang,et al.  On the Convergence Rates of Genetic Algorithms , 1999, Theor. Comput. Sci..

[17]  Xin Yao,et al.  A study of drift analysis for estimating computation time of evolutionary algorithms , 2004, Natural Computing.

[18]  Xin Yao,et al.  A comparative study of three evolutionary algorithms incorporating different amounts of domain knowledge for node covering problem , 2005, IEEE Trans. Syst. Man Cybern. Part C.

[19]  Xin Yao,et al.  From an individual to a population: an analysis of the first hitting time of population-based evolutionary algorithms , 2002, IEEE Trans. Evol. Comput..

[20]  Xin Yao,et al.  Time complexity of evolutionary algorithms for combinatorial optimization: A decade of results , 2007, Int. J. Autom. Comput..

[21]  Enrique Alba,et al.  The exploration/exploitation tradeoff in dynamic cellular genetic algorithms , 2005, IEEE Transactions on Evolutionary Computation.

[22]  Andrew M. Sutton,et al.  Understanding elementary landscapes , 2008, GECCO '08.

[23]  Xin Yao,et al.  Drift analysis and average time complexity of evolutionary algorithms , 2001, Artif. Intell..

[24]  Xin Yao,et al.  A Note on Problem Difficulty Measures in Black-Box Optimization: Classification, Realizations and Predictability , 2007, Evolutionary Computation.

[25]  Thomas Bäck,et al.  Selective Pressure in Evolutionary Algorithms: A Characterization of Selection Mechanisms , 1994, International Conference on Evolutionary Computation.