Efficiency enhancement of genetic algorithms via building-block-wise fitness estimation

This paper studies fitness inheritance as an efficiency enhancement technique for a class of competent genetic algorithms called estimation distribution algorithms. Probabilistic models of important sub-solutions are developed to estimate the fitness of a proportion of individuals in the population, thereby avoiding computationally expensive function evaluations. The effect of fitness inheritance on the convergence time and population sizing are modeled and the speed-up obtained through inheritance is predicted. The results show that a fitness-inheritance mechanism which utilizes information on building-block fitnesses provides significant efficiency enhancement. For additively separable problems, fitness inheritance reduces the number of function evaluations to about half and yields a speed-up of about 1.75-2.25.

[1]  David E. Goldberg,et al.  The compact genetic algorithm , 1999, IEEE Trans. Evol. Comput..

[2]  M. Bulmer The Mathematical Theory of Quantitative Genetics , 1981 .

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

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

[5]  David E. Goldberg,et al.  Genetic Algorithms, Tournament Selection, and the Effects of Noise , 1995, Complex Syst..

[6]  Kalyanmoy Deb,et al.  Messy Genetic Algorithms: Motivation, Analysis, and First Results , 1989, Complex Syst..

[7]  Hideyuki Takagi,et al.  Interactive evolutionary computation: fusion of the capabilities of EC optimization and human evaluation , 2001, Proc. IEEE.

[8]  David E. Goldberg,et al.  The Gambler's Ruin Problem, Genetic Algorithms, and the Sizing of Populations , 1999, Evolutionary Computation.

[9]  Martin Pelikan,et al.  Fitness Inheritance in the Bayesian Optimization Algorithm , 2004, GECCO.

[10]  B. Julstrom,et al.  Design of vector quantization codebooks using a genetic algorithm , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[11]  David E. Goldberg,et al.  Scalability of the Bayesian optimization algorithm , 2002, Int. J. Approx. Reason..

[12]  Brad L. Miller,et al.  Noise, sampling, and efficient genetic algorthms , 1997 .

[13]  Bernard De Baets,et al.  Is Fitness Inheritance Useful for Real-World Applications? , 2003, EMO.

[14]  D. Goldberg,et al.  Don't evaluate, inherit , 2001 .

[15]  D. E. Goldberg,et al.  Simple Genetic Algorithms and the Minimal, Deceptive Problem , 1987 .

[16]  下平 丕作士,et al.  The Genetic and Evolutionary Computation Conference , 2002 .

[17]  Dirk Thierens,et al.  Convergence Models of Genetic Algorithm Selection Schemes , 1994, PPSN.

[18]  Pedro Larrañaga,et al.  Estimation of Distribution Algorithms , 2002, Genetic Algorithms and Evolutionary Computation.

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

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

[21]  Kalyanmoy Deb,et al.  Analyzing Deception in Trap Functions , 1992, FOGA.

[22]  Thomas Bck Generalized convergence models for tournament|and (1; ?)|selection , 1995 .

[23]  Ivo Everts,et al.  Extended Compact Genetic Algorithm , 2004 .

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

[25]  Robert E. Smith,et al.  Fitness inheritance in genetic algorithms , 1995, SAC '95.

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

[27]  Kalyanmoy Deb,et al.  Sufficient conditions for deceptive and easy binary functions , 1994, Annals of Mathematics and Artificial Intelligence.

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

[29]  David E. Goldberg,et al.  Linkage Problem, Distribution Estimation, and Bayesian Networks , 2000, Evolutionary Computation.

[30]  David E. Goldberg,et al.  Fitness Inheritance In Multi-objective Optimization , 2002, GECCO.

[31]  David E. Goldberg,et al.  Bayesian optimization algorithm, decision graphs, and Occam's razor , 2001 .

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

[33]  David E. Goldberg,et al.  Evolutionary Computation As A Form Of Organization , 2002, GECCO.

[34]  David E. Goldberg,et al.  Bayesian optimization algorithm: from single level to hierarchy , 2002 .

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