Advanced neighborhoods and problem difficulty measures

While different measures of problem difficulty of fitness landscapes have been proposed, recent studies have shown that many of the common ones do not closely correspond to the actual difficulty of problems when solved by evolutionary algorithms. One of the reasons for this is that most problem difficulty measures are based on neighborhood structures that are quite different from those used in most evolutionary algorithms. This paper examines several ways to increase the accuracy of problem difficulty measures by including linkage information in the measure to more accurately take into account the advanced neighborhoods explored by some evolutionary algorithms. The effects of these modifications of problem difficulty are examined in the context of several simple and advanced evolutionary algorithms. The results are then discussed and promising areas for future research are proposed.

[1]  David E. Goldberg,et al.  A Survey of Optimization by Building and Using Probabilistic Models , 2002, Comput. Optim. Appl..

[2]  Mohamed Slimane,et al.  A Critical and Empirical Study of Epistasis Measures for Predicting GA Performances: A Summary , 1997, Artificial Evolution.

[3]  Martin Pelikan,et al.  Hierarchical Bayesian optimization algorithm: toward a new generation of evolutionary algorithms , 2010, SICE 2003 Annual Conference (IEEE Cat. No.03TH8734).

[4]  David Maxwell Chickering,et al.  A Bayesian Approach to Learning Bayesian Networks with Local Structure , 1997, UAI.

[5]  P. Stadler Landscapes and their correlation functions , 1996 .

[6]  Nir Friedman,et al.  Learning Bayesian Networks with Local Structure , 1996, UAI.

[7]  Kalyanmoy Deb,et al.  Genetic Algorithms, Noise, and the Sizing of Populations , 1992, Complex Syst..

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

[9]  D. Goldberg,et al.  Domino convergence, drift, and the temporal-salience structure of problems , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[10]  J. A. Lozano,et al.  Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation , 2001 .

[11]  David E. Goldberg,et al.  A hierarchy machine: Learning to optimize from nature and humans , 2003, Complex..

[12]  Franz Rothlauf,et al.  Evaluation-Relaxation Schemes for Genetic and Evolutionary Algorithms , 2004 .

[13]  E. Weinberger,et al.  Correlated and uncorrelated fitness landscapes and how to tell the difference , 1990, Biological Cybernetics.

[14]  D. Goldberg,et al.  Escaping hierarchical traps with competent genetic algorithms , 2001 .

[15]  Martin V. Butz,et al.  Performance of Evolutionary Algorithms on Random Decomposable Problems , 2006, PPSN.

[16]  Terry Jones,et al.  Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms , 1995, ICGA.

[17]  Vassilis Zissimopoulos,et al.  Autocorrelation Coefficient for the Graph Bipartitioning Problem , 1998, Theor. Comput. Sci..

[18]  David E. Goldberg,et al.  The Design of Innovation: Lessons from and for Competent Genetic Algorithms , 2002 .

[19]  G. Harik Linkage Learning via Probabilistic Modeling in the ECGA , 1999 .

[20]  H. Mühlenbein,et al.  From Recombination of Genes to the Estimation of Distributions I. Binary Parameters , 1996, PPSN.

[21]  Peter Merz,et al.  Advanced Fitness Landscape Analysis and the Performance of Memetic Algorithms , 2004, Evolutionary Computation.

[22]  Martin V. Butz,et al.  Performance of evolutionary algorithms on NK landscapes with nearest neighbor interactions and tunable overlap , 2009, GECCO '09.

[23]  Bernd Freisleben,et al.  Greedy and Local Search Heuristics for Unconstrained Binary Quadratic Programming , 2002, J. Heuristics.

[24]  Shumeet Baluja,et al.  A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning , 1994 .

[25]  Andrew M. Sutton,et al.  A polynomial time computation of the exact correlation structure of k-satisfiability landscapes , 2009, GECCO '09.

[26]  Martin Pelikan NK landscapes, problem difficulty, and hybrid evolutionary algorithms , 2010, GECCO '10.

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