Name
Abstract | ||
|---|---|---|
Bee-Swarm genetic algorithm based on reproducing of swarm is a novel improved genetic algorithm Comparing to GA, there are two populations, one for global search, and another for local search Only best one can crossover The genetic operators include order crossover operator, adaptive mutation operator and restrain operator The simulated annealing is also introduced to help local optimization The method sufficiently takes the advantage of genetic algorithm such as group search and global convergence, and quick parallel search can efficiently overcome the problems of local optimization Theoretically, the capability of finding the global optimum is proved, and a necessary and sufficient condition is obtained namely The convergence and effective of BSGA is proved by Markov chain and genetic mechanism Finally, several testing experiments show that the Bee-Swarm genetic algorithm is good. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-13495-1_4 | ICSI (1) |
Keywords | Field | DocType |
bee-swarm genetic algorithm,genetic operator,group search,genetic algorithm,local optimization,genetic mechanism,adaptive mutation operator,global search,quick parallel search,local search,simulated annealing,markov chain,genetics,simulated annealing algorithm | Hill climbing,Genetic operator,Computer science,Artificial intelligence,Population-based incremental learning,Genetic algorithm,Mathematical optimization,Crossover,Premature convergence,Meta-optimization,Algorithm,Cultural algorithm,Machine learning | Conference |
Volume | ISSN | ISBN |
6145 | 0302-9743 | 3-642-13494-7 |
Citations | PageRank | References |
0 | 0.34 | 2 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Di Wu | 1 | 0 | 0.34 |
| Rong-yi Cui | 2 | 0 | 2.70 |
| Changrong Li | 3 | 0 | 0.34 |
| Guangjun Song | 4 | 1 | 0.71 |