Name
Abstract | ||
|---|---|---|
Genetic fitness optimization using small populations or small population
updates across generations generally suffers from randomly diverging
evolutions. We propose a notion of highly probable fitness optimization through
feasible evolutionary computing runs on small size populations. Based on
rapidly mixing Markov chains, the approach pertains to most types of
evolutionary genetic algorithms, genetic programming and the like. We establish
that for systems having associated rapidly mixing Markov chains and appropriate
stationary distributions the new method finds optimal programs (individuals)
with probability almost 1. To make the method useful would require a structured
design methodology where the development of the program and the guarantee of
the rapidly mixing property go hand in hand. We analyze a simple example to
show that the method is implementable. More significant examples require
theoretical advances, for example with respect to the Metropolis filter. |
Year | DOI | Venue |
|---|---|---|
1999 | 10.1016/S0304-3975(99)00263-7 | Clinical Orthopaedics and Related Research |
Keywords | DocType | Volume |
evolutionary programming,evolutionary computing,stationary distribution,genetics,evolutionary genetics | Journal | 241 |
Issue | ISSN | Citations |
1-2 | Theoretical Computer Science | 5 |
PageRank | References | Authors |
0.46 | 17 | 1 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Paul Vitányi | 1 | 2130 | 287.76 |