Name
Abstract | ||
|---|---|---|
In this paper, a mutation with cycle probability is designed by simulating the evolutionary rule of the earth creature, and a genetic algorithm based on the cycle mutation, presents the ability in improving search efficiency and overcoming premature to some extent. To further improve performance of the algorithm, the selection is mended according to the phenomena that optimum individual always plays a major role, and an improved cycle mutation genetic algorithm is proposed. The experiment results on the benchmark functions optimization show that exploration and exploitation of this algorithm is better than some well-known evolution algorithms and it is not sensitive to the initial population distribution. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ICTAI.2008.30 | ICTAI (2) |
Keywords | Field | DocType |
cycle probability,well-known evolution algorithm,initial population distribution,earth creature,cycle mutation,genetic algorithm,experiment result,benchmark function,evolutionary rule,improved cycle mutation,self-organization genetic algorithm,genetic algorithms,search problem,algorithm design and analysis,self organization,testing,probability,optimization,gallium | Population,Algorithm design,Computer science,Self-organization,Mutation (genetic algorithm),Artificial intelligence,Cultural algorithm,Search problem,Population-based incremental learning,Machine learning,Genetic algorithm | Conference |
ISSN | Citations | PageRank |
1082-3409 | 0 | 0.34 |
References | Authors | |
5 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Na Wang | 1 | 0 | 0.68 |
| Jian Zhuang | 2 | 104 | 15.09 |
| Haifeng Du | 3 | 421 | 30.96 |
| Sun'an Wang | 4 | 34 | 5.40 |