Accession Number : ADA302967

Title :   An Empirical Comparison of Seven Iterative and Evolutionary Function Optimization Heuristics,

Corporate Author : CARNEGIE-MELLON UNIV PITTSBURGH PA DEPT OF COMPUTER SCIENCE

Personal Author(s) : Baluja, Shumeet

PDF Url : ADA302967

Report Date : 01 SEP 1995

Pagination or Media Count : 22

Abstract : This report is a repository of the results obtained from a large scale empirical comparison of seven iterative and evolution-based optimization heuristics. Twenty-seven static optimization problems, spanning six sets of problem classes which are commonly explored in genetic algorithm literature, are examined. The problem sets include job-shop scheduling, traveling, salesman, knapsack, binpacking, neural network weight optimization, and standard numerical optimization. The search spaces in these problems range from 2368 to 22040. The results indicate that using genetic algorithms for the optimization of static functions does not yield a benefit, in terms of the final answer obtained, over simpler optimization heuristics. Descriptions of the algorithms tested and the encodings of the problems are described in detail for reproducibility.

Descriptors :   *OPTIMIZATION, *HEURISTIC METHODS, *ITERATIONS, ALGORITHMS, FUNCTIONS, NETHERLANDS, COMPARISON, GASES, VARIATIONS, EVOLUTION(GENERAL), HYBRID SYSTEMS, RANGE(EXTREMES), STATICS, GENETICS.

Subject Categories : Operations Research

Distribution Statement : APPROVED FOR PUBLIC RELEASE