Parameter tuning of PBIL and CHC evolutionary algorithms applied to solve the Root Identification Problem

Evolutionary algorithms are among the most successful approaches for solving a number of problems where systematic searches in huge domains must be performed. One problem of practical interest that falls into this category is known as The Root Identification Problem in Geometric Constraint Solving, where one solution to the geometric problem must be selected among a number of possible solutions bounded by an exponential number. In previous works we have shown that applying genetic algorithms, a category of evolutionary algorithms, to solve the Root Identification Problem is both feasible and effective. In this work, we report on an empirical statistical study conducted to establish the influence of the driving parameters in the PBIL and CHC evolutionary algorithms when they are used to solve the Root Identification Problem. We identify a set of values that optimize algorithms performance. The driving parameters considered for the PBIL algorithm are population size, mutation probability, mutation shift and learning rate. For the CHC algorithm we studied population size, divergence rate, differential threshold and the set of best individuals. In both cases we applied unifactorial and multifactorial analysis, post hoc tests and best parameter level selection. Experimental results show that CHC outperforms PBIL when applied to solve the Root Identification Problem.

[1]  Gilbert Laporte,et al.  Metaheuristics: A bibliography , 1996, Ann. Oper. Res..

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

[3]  Thomas Stützle,et al.  Stochastic Local Search: Foundations & Applications , 2004 .

[4]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

[5]  Robert Joan-Arinyo,et al.  Combining constructive and equational geometric constraint-solving techniques , 1999, TOGS.

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

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

[8]  J. K. Lenstra,et al.  Local Search in Combinatorial Optimisation. , 1997 .

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

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

[11]  Jay L. Devore,et al.  Probability and statistics for engineering and the sciences , 1982 .

[12]  Christoph M. Hoffmann,et al.  A graph-constructive approach to solving systems of geometric constraints , 1997, TOGS.

[13]  M. Victoria Luzón,et al.  Searching the Solution Space in Constructive Geometric Constraint Solving with Genetic Algorithms , 2005, Applied Intelligence.

[14]  A A Tsiatis,et al.  Exact significance testing to establish treatment equivalence with ordered categorical data. , 1984, Biometrics.

[15]  Enrique Barreiro Alonso Modelización y optimización de algoritmos genéticos para la selección de la solución deseada en resolución constructiva de restricciones geométricas , 2006 .

[16]  Zbigniew Michalewicz,et al.  Handbook of Evolutionary Computation , 1997 .

[17]  George C. Canavos,et al.  Applied probability and statistical methods , 1984 .

[18]  Zbigniew Michalewicz,et al.  Parameter Setting in Evolutionary Algorithms , 2007, Studies in Computational Intelligence.

[19]  S.J.J. Smith,et al.  Empirical Methods for Artificial Intelligence , 1995 .

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

[21]  Pierre Hansen,et al.  Variable neighborhood search: Principles and applications , 1998, Eur. J. Oper. Res..

[22]  D. Cooke,et al.  A Basic Course in Statistics , 2000 .

[23]  James P. Braselton,et al.  Multiple Comparison Methods for Means , 2002, SIAM Rev..

[24]  M. Victoria Luzón,et al.  Genetic algorithms for root multiselection in constructive geometric constraint solving , 2003, Comput. Graph..

[25]  B. Tabachnick,et al.  Using Multivariate Statistics , 1983 .

[26]  Caroline Essert,et al.  Sketch-based pruning of a solution space within a formal geometric constraint solver , 2000, Artif. Intell..

[27]  W. Hoeffding,et al.  Contributions to Probability and Statistics: Essays in Honor of Harold Hotelling , 1961 .

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

[29]  William M. K. Trochim,et al.  Research methods knowledge base , 2001 .

[30]  C. Reeves Modern heuristic techniques for combinatorial problems , 1993 .

[31]  N. Draper,et al.  Applied Regression Analysis , 1967 .

[32]  John W. Tukey,et al.  Exploratory Data Analysis. , 1979 .

[33]  Thomas Stützle,et al.  Ant Colony Optimization , 2009, EMO.

[34]  Ravindra K. Ahuja,et al.  Use of Representative Operation Counts in Computational Testing of Algorithms , 1996, INFORMS J. Comput..

[35]  A. E. Eiben,et al.  Self-adaptivity for constraint satisfaction: learning penalty functions , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[36]  Christoph M. Hoffmann,et al.  Geometric constraint solver , 1995, Comput. Aided Des..

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

[38]  M. Victoria Luzón,et al.  Constructive Geometric Constraint Solving: A New Application of Genetic Algorithms , 2002, PPSN.

[39]  David J. Sheskin,et al.  Handbook of Parametric and Nonparametric Statistical Procedures , 1997 .

[40]  Bruno Sareni,et al.  Fitness sharing and niching methods revisited , 1998, IEEE Trans. Evol. Comput..

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

[42]  J. F. Howell,et al.  Pairwise Multiple Comparison Procedures with Unequal N’s and/or Variances: A Monte Carlo Study , 1976 .

[43]  Thomas Stützle,et al.  Classification of Metaheuristics and Design of Experiments for the Analysis of Components , 2001 .

[44]  C. Hoffmann,et al.  A Brief on Constraint Solving , 2005 .