| タイトル | An Empirical Comparison of Seven Iterative and Evolutionary Function Optimization Heuristics |
| 本文（外部サイト） | http://hdl.handle.net/2060/19960035829 |
| 著者（英） | Baluja, Shumeet |
| 著者所属（英） | Carnegie-Mellon Univ. |
| 発行日 | 1995-09-01 |
| 言語 | eng |
| 内容記述 | 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. |
| NASA分類 | Computer Programming and Software |
| レポートNO | 96N30532
NASA-CR-201901
NAS 1.26:201901
AD-A302967
CMU-CS-95-193 |
| 権利 | No Copyright |
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