Name
Abstract | ||
|---|---|---|
It is known that modelling a finite population genetic algorithmas a Markov chain requires a prohibitively large number of states.In an attempt to resolve this problem, a number of state aggregationtechniques have been proposed. We consider two different strategies for aggregating populations, one using equal average fitness and theother using equal best fitness. We examine how the approximation scales with population size, in addition to studying the effects of other parameters (such as mutation rate). We find that a large reduction in the number of states is possible, sometimes with surprisingly small loss of accuracy. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1023/B:NACO.0000023414.30362.8a | Natural Computing |
Keywords | Field | DocType |
aggregation,genetic algorithms,Markov chain,Markov processes | Population,Mathematical optimization,Markov process,Mutation rate,Markov model,Markov chain,Population size,Variable-order Markov model,Mathematics,Genetic algorithm | Journal |
Volume | Issue | ISSN |
3 | 1 | 1572-9796 |
Citations | PageRank | References |
8 | 0.90 | 7 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cheah C. J. Moey | 1 | 13 | 1.36 |
| Jonathan E. Rowe | 2 | 458 | 56.35 |