Name
Abstract | ||
|---|---|---|
Several recent works have examined the effectiveness of using knowledge models to guide search algorithms in high dimensional spaces. It seems that it may be a promising way to tackle some difficult problem. The aim of such methods is to reach good solutions using simultaneously evolutionary search and knowledge guidance. The idea proposed in this paper is to use a bayesian network in order to store and apply the knowledge model and, as a consequence, to accelerate the search process. A traditional evolutionary algorithm is modified in order to allow the reuse of the capitalized knowledge. The approach has been applied to a problem of selection of project scenarios in a multi-objective context. A preliminary version of this method was presented at EA' 07 conference [1]. An experimentation platform has been developed to validate the approach and to study different modes of knowledge injection. The obtained experimental results are presented. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-14156-0_9 | Artificial Evolution |
Keywords | Field | DocType |
difficult problem,knowledge guidance,knowledge injection,knowledge model,search algorithm,search process,evolutionary optimization,traditional evolutionary algorithm,evolutionary search,bayesian network,knowledge integration,capitalized knowledge,a priori knowledge,evolutionary algorithm,project management | Search algorithm,Evolutionary algorithm,Reuse,Computer science,A priori and a posteriori,Bayesian network,Artificial intelligence,Evolutionary programming,Evolutionary music,Machine learning,Project management | Conference |
Volume | ISSN | ISBN |
5975 | 0302-9743 | 3-642-14155-2 |
Citations | PageRank | References |
0 | 0.34 | 9 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Paul Pitiot | 1 | 16 | 3.80 |
| Thierry Coudert | 2 | 27 | 7.91 |
| Laurent Geneste | 3 | 107 | 14.82 |
| Claude Baron | 4 | 36 | 12.88 |