Name
Abstract | ||
|---|---|---|
This work describes a new way of employing problem-specific heuristics to improve evolutionary algorithms: the Population Training Heuristic (PTH). The PTH employs heuristics in fitness definition, guid- ing the population to settle down in search areas where the individuals can not be improved by such heuristics. Some new theoretical improve- ments not present in early algorithms are now introduced. An application for pattern sequencing problems is examined with new improved compu- tational results. The method is also compared against other approaches, using benchmark instances taken from the literature. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/978-3-540-31996-2_16 | EvoWorkshops |
Keywords | Field | DocType |
population training heuristics,benchmark instance,population training heuristic,problem-specific heuristics,fitness definition,new improved computational result,gmlp.,search area,hybrid evolutionary algorithms,population training,evolutionary algorithm,early algorithm,mosp,new theoretical improvement | Population,Heuristic,Evolutionary algorithm,Computer science,Combinatorial optimization,Heuristics,Artificial intelligence,Genetic algorithm | Conference |
Volume | ISSN | ISBN |
3448 | 0302-9743 | 3-540-25337-8 |
Citations | PageRank | References |
4 | 0.55 | 9 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alexandre César Muniz De Oliveira | 1 | 83 | 8.30 |
| Luiz Antonio Nogueira Lorena | 2 | 498 | 36.72 |