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Maintaining diversity in genetic search (1984)
| Content Provider | CiteSeerX |
|---|---|
| Author | Mauldin, Michael L. |
| Description | Genetic adaptive algorithms provide an efficient way to search large function spaces, and are increasingly being used in learning systems. One problem plaguing genetic learning algorithms is premature convergence, or convergence of the pool of active structures to a sub-optimal point in the space being searched. An improvement to the standard genetic adaptive algorithm is presented which guarantees diversity of the gene pool throughout the search. Maintaining genetic diversity is shown to improve off-line (or best) performance of these algorithms at the expense of poorer on-line (or average) performance, and to retard or prevent premature convergence. 1. Int reduction Genetic adaptive algorithms (GA’s) are one solution to the |
| File Format | |
| Language | English |
| Publisher | Kaufmann |
| Publisher Date | 1984-01-01 |
| Access Restriction | Open |
| Subject Keyword | Int Reduction Genetic Adaptive Algorithm Active Structure Premature Convergence Gene Pool Sub-optimal Point Genetic Adaptive Algorithm Maintaining Genetic Diversity Efficient Way Standard Genetic Adaptive Algorithm Genetic Search Large Function Space Genetic Learning Algorithm |
| Content Type | Text |
| Resource Type | Article |
