Name
Abstract | ||
|---|---|---|
We propose a 2-population cooperative coevolutionary optimization method that can efficiently solve multi-objective optimization problems as it successfully combines positive traits from classic multi-objective evolutionary algorithms and from newer optimization approaches that explore the concept of differential evolution. A key part of the algorithm lies in the proposed dual fitness sharing mechanism that is able to smoothly transfer information between the two coevolved populations without negatively impacting the independent evolutionary process behavior that characterizes each population. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-642-53856-8_32 | EUROCAST (1) |
Keywords | Field | DocType |
continuous multi-objective optimization, evolutionary algorithms, cooperative coevolution, differential evolution | Population,Mathematical optimization,Evolutionary algorithm,Computer science,Cooperative coevolution,Evolutionary computation,Differential evolution,Multi-objective optimization,Multi-swarm optimization,Artificial intelligence,Optimization problem,Machine learning | Conference |
Volume | ISSN | Citations |
8111 | 0302-9743 | 3 |
PageRank | References | Authors |
0.43 | 13 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alexandru-Ciprian Zavoianu | 1 | 48 | 6.37 |
| Edwin Lughofer | 2 | 1940 | 99.72 |
| Wolfgang Amrhein | 3 | 78 | 8.29 |
| Erich Peter Klement | 4 | 989 | 128.89 |