Name
Abstract | ||
|---|---|---|
A frequently observed difficulty in the application of genetic algorithms to the domain of optimization arises from premature convergence. In order to preserve genotype diversity we develop a new model of auto-adaptive behavior for individuals. In this model a population member is an active individual that assumes social-like behavior patterns. Different individuals living in the same population can assume different patterns. By moving in a hierarchy of social states individuals change their behavior. Changes of social state are controlled by arguments of plausibility. These arguments are implemented as a rule set for a massively-parallel genetic algorithm. Computational experiments on 12 large-scale job shop benchmark problems show that the results of the new approach dominate the ordinary genetic algorithm significantly. |
Year | DOI | Venue |
|---|---|---|
1994 | 10.1007/3-540-58484-6_246 | PPSN |
Keywords | Field | DocType |
parallel population dynamics,social-like behavior,computer experiment,population dynamic,premature convergence,behavior change,genetic algorithm,adaptive behavior | Population Member,Population,Mathematical optimization,Premature convergence,Computer science,Job shop,Artificial intelligence,Hierarchy,Population-based incremental learning,Machine learning,Genetic algorithm | Conference |
Volume | ISSN | ISBN |
866 | 0302-9743 | 3-540-58484-6 |
Citations | PageRank | References |
15 | 3.22 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dirk C. Mattfeld | 1 | 283 | 26.47 |
| Herbert Kopfer | 2 | 499 | 60.75 |
| Christian Bierwirth | 3 | 586 | 38.75 |