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

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

Efficiency Enhancement of Genetic Algorithms via Building-Block-Wise Fitness Estimation Kumara Sastry Martin Pelikan David E. Goldberg IlliGAL Report No.February, 2004 Illinois Genetic Algorithms Laboratory (IlliGAL) Department of General Engineering University of Illinois at Urbana-Champaign 117 Transportation Building 104 S. Mathews Avenue, Urbana, IL 61801 Efficiency Enhancement of Genetic Algorithms via Building-Block-Wise Fitness Estimation Kumara Sastry Illinois Genetic Algorithms Laboratory (IlliGAL), and Department of Material Science Engineering University of Illinois at Urbana-Champaign ksastry@uiuc.edu Martin Pelikan Department of Math Computer Science University of Missouri, St. Louis mpelikan@cs.umsl.edu David E. Goldberg Illinois Genetic Algorithms Laboratory (IlliGAL), and Department of General Engineering University of Illinois at Urbana-Champaign deg@uiuc.edu Abstract 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 Introduction Since the inception of genetic and evolutionary algorithms (GEAs), significant advances have been made in the theory, design and application to complex real-world problems. A decomposition methodology has been proposed for a successful design of GEA