Name
Abstract | ||
|---|---|---|
The quantum-inspired evolutionary algorithm (QEA) applies several quantum computing principles to solve optimization problems. In QEA, a population of probabilistic models of promising solutions is used to guide further exploration of the search space. This paper clearly establishes that QEA is an original algorithm that belongs to the class of estimation of distribution algorithms (EDAs), while the common points and specifics of QEA compared to other EDAs are highlighted. The behavior of a versatile QEA relatively to three classical EDAs is extensively studied and comparatively good results are reported in terms of loss of diversity, scalability, solution quality, and robustness to fitness noise. To better understand QEA, two main advantages of the multimodel approach are analyzed in details. First, it is shown that QEA can dynamically adapt the learning speed leading to a smooth and robust convergence behavior. Second, we demonstrate that QEA manipulates more complex distributions of solutions than with a single model approach leading to more efficient optimization of problems with interacting variables. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/TEVC.2008.2003010 | IEEE Trans. Evolutionary Computation |
Keywords | Field | DocType |
single model approach,quantum-inspired evolutionary algorithm,versatile qea,efficient optimization,robust convergence behavior,optimization problem,classical edas,distribution algorithm,multimodel eda,multimodel approach,original algorithm,genetic algorithms,quantum computing,estimation theory,scalability,optimization,ant colony optimization,search space,space technology,estimation of distribution algorithm,mutual information,space exploration,clustering algorithms,evolutionary computation,quantum computer,probabilistic model | Ant colony optimization algorithms,Population,Evolutionary algorithm,Estimation of distribution algorithm,Artificial intelligence,Optimization problem,Genetic algorithm,EDAS,Mathematical optimization,Algorithm,Evolutionary computation,Mathematics,Machine learning | Journal |
Volume | Issue | ISSN |
13 | 6 | 1089-778X |
Citations | PageRank | References |
60 | 1.98 | 38 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael Defoin-platel | 1 | 152 | 8.82 |
| Stefan Schliebs | 2 | 380 | 18.56 |
| Nikola K Kasabov | 3 | 3645 | 290.73 |