Evaluating performance advantages of grouping genetic algorithms
| Autor: | Evelyn C. Brown, Robert T. Sumichrast |
|---|---|
| Rok vydání: | 2005 |
| Předmět: |
Mathematical optimization
Bin packing problem Computer science media_common.quotation_subject Minor (linear algebra) Constrained optimization Range (mathematics) Artificial Intelligence Control and Systems Engineering Problem domain Genetic algorithm Quality (business) Electrical and Electronic Engineering media_common |
| Zdroj: | Engineering Applications of Artificial Intelligence. 18:1-12 |
| ISSN: | 0952-1976 |
| DOI: | 10.1016/j.engappai.2004.08.024 |
| Popis: | The genetic algorithm (GA) and a related procedure called the grouping genetic algorithm (GGA) are solution methodologies used to search for optimal solutions in constrained optimization problems. While the GA has been successfully applied to a range of problem types, the GGA was created specifically for problems involving the formation of groups. Falkenauer (JORBEL-Belg. J. Oper. Res. Stat. Comput. Sci. 33 (1992) 79), the originator of the GGA, and subsequent researchers have proposed reasons for expecting the GGA to perform more efficiently than the GA on grouping problems. Yet, there has been no research published to date which tests claims of GGA superiority. This paper describes empirical tests of the performance of GA and GGA in three domains which have substantial, practical importance, and which have been the subject of considerable academic research. Our purpose is not to determine which of these two approaches is better across an entire problem domain, but rather to begin to document practical differences between a standard off-the-shelf GA and a tailored GGA. Based on the level of solution quality desired, it may be the case that the additional time and resources required to design a tailored GGA may not be justified if the improvement in solution quality is only minor or non-existent. |
| Databáze: | OpenAIRE |
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