Evaluating Evolutionary Algorithms

Test functions are commonly used to evaluate the effectiveness of different search algorithms. However, the results of evaluation are as dependent on the test problems as they are on the algorithms that are the subject of comparison. Unfortunately, developing a test suite for evaluating competing search algorithms is difficult without clearly defined evaluation goals. In this paper we discuss some basic principles that can be used to develop test suites and we examine the role of test suites as they have been used to evaluate evolutionary search algorithms. Current test suites include functions that are easily solved by simple search methods such as greedy hill-climbers. Some test functions also have undesirable characteristics that are exaggerated as the dimensionality of the search space is increased. New methods are examined for constructing functions with different degrees of nonlinearity, where the interactions and the cost of evaluation scale with respect to the dimensionality of the search space.

[1]  H. H. Rosenbrock,et al.  An Automatic Method for Finding the Greatest or Least Value of a Function , 1960, Comput. J..

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

[3]  L. Darrell Whitley,et al.  Changing Representations During Search: A Comparative Study of Delta Coding , 1994, Evolutionary Computation.

[4]  Fred W. Glover,et al.  Tabu Search - Part I , 1989, INFORMS J. Comput..

[5]  Darrell Whitley A genetic algorithm tutorial , 1994 .

[6]  Georges R. Harik,et al.  Foundations of Genetic Algorithms , 1997 .

[7]  Nicholas J. Radcliffe Genetic neural networks on MIMD computers , 1992 .

[8]  Lawrence Davis,et al.  Training Feedforward Neural Networks Using Genetic Algorithms , 1989, IJCAI.

[9]  D. Ackley A connectionist machine for genetic hillclimbing , 1987 .

[10]  Kenneth Alan De Jong,et al.  An analysis of the behavior of a class of genetic adaptive systems. , 1975 .

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

[12]  Gilbert Syswerda,et al.  A Study of Reproduction in Generational and Steady State Genetic Algorithms , 1990, Foundations of Genetic Algorithms.

[13]  Kenneth A. De Jong Genetic Algorithms are NOT Function Optimizers , 1992, FOGA.

[14]  David E. Goldberg,et al.  A Note on Boltzmann Tournament Selection for Genetic Algorithms and Population-Oriented Simulated Annealing , 1990, Complex Syst..

[15]  Thomas Bäck,et al.  An Overview of Evolutionary Algorithms for Parameter Optimization , 1993, Evolutionary Computation.

[16]  K. Dejong,et al.  An analysis of the behavior of a class of genetic adaptive systems , 1975 .

[17]  Schloss Birlinghoven Evolution in Time and Space -the Parallel Genetic Algorithm , 1991 .

[18]  Lawrence Davis,et al.  Genetic Algorithms and Simulated Annealing , 1987 .

[19]  Rajarshi Das,et al.  A Study of Control Parameters Affecting Online Performance of Genetic Algorithms for Function Optimization , 1989, ICGA.

[20]  D. Anderson,et al.  Algorithms for minimization without derivatives , 1974 .

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

[22]  Ellis Horowitz,et al.  Fundamentals of Computer Algorithms , 1978 .

[23]  John H. Holland,et al.  Adaptation in natural and artificial systems , 1975 .

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

[25]  L. Darrell Whitley,et al.  Staged hybrid genetic search for seismic data imaging , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[26]  R. Brent Table errata: Algorithms for minimization without derivatives (Prentice-Hall, Englewood Cliffs, N. J., 1973) , 1975 .

[27]  M. J. D. Powell,et al.  An Iterative Method for Finding Stationary Values of a Function of Several Variables , 1962, Comput. J..

[28]  David B. Fogel Evolutionary programming: an introduction and some current directions , 1994 .

[29]  Paolo Toth,et al.  Knapsack Problems: Algorithms and Computer Implementations , 1990 .

[30]  Kennetb A. De Genetic Algorithms Are NOT Function Optimizers , 1992 .

[31]  J. D. Schaffer,et al.  Combinations of genetic algorithms and neural networks: a survey of the state of the art , 1992, [Proceedings] COGANN-92: International Workshop on Combinations of Genetic Algorithms and Neural Networks.

[32]  Heinz Mühlenbein,et al.  Evolution in Time and Space - The Parallel Genetic Algorithm , 1990, Foundations of Genetic Algorithms.

[33]  John N. Hooker,et al.  Testing heuristics: We have it all wrong , 1995, J. Heuristics.

[34]  Lawrence Davis,et al.  Bit-Climbing, Representational Bias, and Test Suite Design , 1991, International Conference on Genetic Algorithms.

[35]  Ingo Rechenberg,et al.  Evolutionsstrategie : Optimierung technischer Systeme nach Prinzipien der biologischen Evolution , 1973 .

[36]  L. Darrell Whitley,et al.  Transforming the search space with Gray coding , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[37]  Thomas Bäck,et al.  Genetic Algorithms and Evolution Strategies - Similarities and Differences , 1990, PPSN.

[38]  Thomas Bäck,et al.  A Survey of Evolution Strategies , 1991, ICGA.

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

[40]  L. Darrell Whitley,et al.  Genetic algorithms and neural networks: optimizing connections and connectivity , 1990, Parallel Computing.

[41]  Heinz Mühlenbein,et al.  Predictive Models for the Breeder Genetic Algorithm I. Continuous Parameter Optimization , 1993, Evolutionary Computation.

[42]  Melanie Mitchell,et al.  Relative Building-Block Fitness and the Building Block Hypothesis , 1992, Foundations of Genetic Algorithms.

[43]  L. Darrell Whitley,et al.  Building Better Test Functions , 1995, ICGA.

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