Name
Abstract | ||
|---|---|---|
The theory of chaos optimization is introduced in this paper; and through improving the constringency strategy of mutative scale chaos optimization method, we can enhance the efficiency and the performance of chaos optimization method; then aiming at the trouble of easy getting into local minimum existed in quantum genetic algorithm, this paper presents a new chaos quantum genetic algorithm. Using the improved mutative scale chaos optimization method, chaotic search for the optimization is implemented to the population which is processed one time with the quantum genetic algorithm, which can lead to the rapid evolution of the population. The test of typical function shows that the performance of this method is better than quantum genetic algorithm and genetic algorithm. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ICNC.2008.739 | ICNC |
Keywords | Field | DocType |
mutative scale chaos optimization,genetic algorithm,chaos optimization method,quantum genetic algorithm,optimization quantum genetic algorithm,improved mutative scale chaos,constringency strategy,chaos optimization,optimization method,new chaos quantum,chaotic search,optimization,gallium,convergence,quantum computing,genetic algorithms | Convergence (routing),Population,Mathematical optimization,Computer science,Meta-optimization,Quantum genetic algorithm,Quantum computer,Algorithm,Chaos optimization,Chaotic search,Genetic algorithm | Conference |
Citations | PageRank | References |
1 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hao Teng | 1 | 1 | 2.37 |
| Baohua Zhao | 2 | 477 | 49.68 |
| Bingru Yang | 3 | 186 | 26.67 |