Name
Abstract | ||
|---|---|---|
A genetic recombination framework is presented within which both the unit of inher- itance of genetic material from a parent, and the number of parents involved in the creation of a new individual are potentially learnt through the evolution of competing subpopulations representing different strategies. At the heart of the framework is a recombination mechanism whereby a newly cre- ated member of the population may be the result of the conjunction of genetic mate- rial from any number of parents, from a low of one up to a maximum limited by the number of genes in the individual chromosome. This is achieved by the use of a rep- resentation with genotypically encoded "links" between adjacent genes, which are respected during recombination. Initially the system contains subpopulations with differing degrees of linkage, and these link bits are subject to mutation. Thus the framework encompasses a variety of strategies from population based random muta- tion hill-climbing through to the simultaneous parallel optimisation of each locus individually cf. Syswerda's Simulated Crossover, and the competition between sub- populations representing these strategies allows the amount and type of recombina- tion to adapt as the search progresses. The performance of the operator is demonstrated by comparisons with other com- mon recombination strategies over a range of function optimisation problems designed to illustrate a variety of degrees of epistasis and deception. The flexibility of the operator in terms of various parameter settings is investigated, and an analysis is given of the different strategies adopted by the framework to solve different classes of problems. |
Year | DOI | Venue |
|---|---|---|
1995 | 10.1007/3-540-60469-3_24 | Evolutionary Computing, AISB Workshop |
Keywords | Field | DocType |
adaptive poly-parental recombination strategy,hill climbing,genetics | Recombination,Mutation rate,Computer science,Recombination operators,Genetic recombination,Artificial intelligence,Computational biology,Genetic algorithm | Conference |
ISBN | Citations | PageRank |
3-540-60469-3 | 20 | 1.90 |
References | Authors | |
15 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| jim smith fogarty | 1 | 20 | 1.90 |
| james l d smith | 2 | 20 | 1.90 |
| T C Fogarty | 3 | 1147 | 152.53 |