genetic algorithms (ga) are a well-known tool used to obtain approximate solutions to optimization problems. successful application of genetic algorithm in solving given problem is largely dependant on selecting appropriate genetic operators. selection, mutation and crossover techniques play a fundamental role in both time needed to obtain results and their accuracy. in this paper we focus on applying genetic algorithms in calculating (edge) search number and search strategy for general graphs . our genetic representation of problem domain is based on representing search strategy as a permutation of edges and fitness function is based on the number of searchers needed to perform a given strategy. our implementation of ga is utilized to compute search strategies for selected graph classes. we compare and discuss results obtained while employing different reproduction strategies.
Autorzy
- Łukasz Wrona,
- Bartosz Jaworski
Informacje dodatkowe
- Kategoria
- Publikacja w czasopiśmie
- Typ
- artykuły w czasopismach recenzowanych i innych wydawnictwach ciągłych
- Język
- angielski
- Rok wydania
- 2010
