Name
Abstract | ||
|---|---|---|
Our theoretical understanding of the properties of genetic algorithms (GAs) being used for function optimization (GAFOs) is not as strong as we would like. Traditional schema analysis provides some first order insights, but doesn't capture the non-linear dynamics of the GA search process very well. Markov chain theory has been used primarily for steady state analysis of GAs. In this paper we explore the use of transient Markov chain analysis to model and understand the behavior of finite population GAFOs observed while in transition to steady states. This approach appears to provide new insights into the circumstances under which GAFOs will (will not) perform well. Some preliminary results are presented and an initial evaluation of the merits of this approach is provided. |
Year | Venue | Keywords |
|---|---|---|
1994 | FOGA | markov chain,non linear dynamics,first order,steady state,genetic algorithm,steady state analysis |
Field | DocType | Citations |
Markov chain mixing time,Mathematical optimization,Markov process,Continuous-time Markov chain,Computer science,Markov model,Markov chain,Balance equation,Variable-order Markov model,Markov kernel | Conference | 44 |
PageRank | References | Authors |
4.54 | 13 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kenneth De Jong | 1 | 3798 | 525.78 |
| William M. Spears | 2 | 1742 | 214.48 |
| Diana F. Gordon | 3 | 502 | 70.20 |