Empirical Study: Initial Population Diversity and Genetic Algorithm Performance

This article presents an empirical study regarding the hypothesis that higher diversity in initial populations for Genetic Algorithms can reduce the number of iterations required to reach an optimum and potentially increase solution quality. We develop the empirical study using some theoretical functions addressed by other researchers such that the input to the Genetic Algorithm is populations of differing diversity. It is expected that the effort in analyzing the initial population with a diversity measure is going to be compensated for by reducing the number of iterations required and perhaps improving solution quality.

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

[2]  Alan Piszcz,et al.  Genetic programming: optimal population sizes for varying complexity problems , 2006, GECCO '06.

[3]  Pedro A. Diaz-Gomez,et al.  Initial Population for Genetic Algorithms: A Metric Approach , 2007, GEM.

[4]  J.T. Alander,et al.  On optimal population size of genetic algorithms , 1992, CompEuro 1992 Proceedings Computer Systems and Software Engineering.

[5]  Fernando G. Lobo,et al.  A parameter-less genetic algorithm , 1999, GECCO.

[6]  Robert L. Sedlmeyer,et al.  The Hamming metric in genetic algorithms and its application to two network problems , 1993, SAC '93.

[7]  D. Goldberg,et al.  Population Sizing for Entropy-based Model Building in Genetic Algorithms , 2006 .

[8]  David E. Goldberg,et al.  The parameter-less genetic algorithm in practice , 2004, Inf. Sci..

[9]  David E. Goldberg,et al.  Bayesian Optimization Algorithm, Population Sizing, and Time to Convergence , 2000, GECCO.

[10]  C. E. SHANNON,et al.  A mathematical theory of communication , 1948, MOCO.

[11]  Kalyanmoy Deb,et al.  Massive Multimodality, Deception, and Genetic Algorithms , 1992, PPSN.

[12]  V. K. Koumousis,et al.  A saw-tooth genetic algorithm combining the effects of variable population size and reinitialization to enhance performance , 2006, IEEE Transactions on Evolutionary Computation.

[13]  Cláudio F. Lima,et al.  A review of adaptive population sizing schemes in genetic algorithms , 2005, GECCO '05.

[14]  Thomas Bäck,et al.  An Empirical Study on GAs "Without Parameters" , 2000, PPSN.

[15]  A. Rosa,et al.  An experimental study on dynamic random variation of population size , 1999, IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028).

[16]  Yee Leung,et al.  Degree of population diversity - a perspective on premature convergence in genetic algorithms and its Markov chain analysis , 1997, IEEE Trans. Neural Networks.

[17]  David B. Fogel,et al.  Evolutionary algorithms in theory and practice , 1997, Complex.

[18]  Colin R. Reeves,et al.  Using Genetic Algorithms with Small Populations , 1993, ICGA.

[19]  Zbigniew Michalewicz,et al.  GAVaPS-a genetic algorithm with varying population size , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[20]  Graham Kendall,et al.  Diversity in genetic programming: an analysis of measures and correlation with fitness , 2004, IEEE Transactions on Evolutionary Computation.

[21]  Zbigniew Michalewicz,et al.  Parameter Control in Evolutionary Algorithms , 2007, Parameter Setting in Evolutionary Algorithms.

[22]  Dorothea Heiss-Czedik,et al.  An Introduction to Genetic Algorithms. , 1997, Artificial Life.

[23]  Lothar Thiele,et al.  Comparison of Multiobjective Evolutionary Algorithms: Empirical Results , 2000, Evolutionary Computation.