Name
Abstract | ||
|---|---|---|
Competitive coevolutionary algorithms are stochastic population-based search algorithms. To date, most competitive coevolution research has been carried in the domain of single-objective optimization. We propose a novel competitive coevolutionary framework to explore Pareto-based multi objective competitive coevolution. This framework utilizes the hypervolume indicator and fitness sharing mechanism to address disengagement and over-specialisation issues. A diversity-driven evolutionary selection scheme is utilized to deal with the loss of fitness gradient problem. Several series of experiments are conducted using multi-objective two-sided competitive games. The results suggest that Pareto-optimal solutions can effectively be found using our proposed coevolutionary framework. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/CEC.2011.5949912 | IEEE Congress on Evolutionary Computation |
Keywords | Field | DocType |
fitness gradient problem,pareto-based multiobjective competitive coevolutionary dynamics,stochastic processes,evolutionary computation,stochastic population-based search algorithms,fitness sharing mechanism,single-objective optimization,search problems,pareto optimisation,game theory,gradient methods,diversity-driven evolutionary selection scheme,multiobjective two-sided competitive games,hypervolume indicator,games,evolutionary computing,sorting,measurement,optimization | Population,Competitive coevolution,Mathematical optimization,Search algorithm,Computer science,Evolutionary computation,Stochastic process,Sorting,Game theory,Artificial intelligence,Pareto principle,Machine learning | Conference |
ISSN | ISBN | Citations |
Pending | 978-1-4244-7834-7 | 0 |
PageRank | References | Authors |
0.34 | 8 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fanchao Zeng | 1 | 13 | 2.59 |
| James Decraene | 2 | 50 | 10.17 |
| Malcolm Yoke Hean Low | 3 | 694 | 52.19 |
| Wentong Cai | 4 | 1928 | 197.81 |
| Philip Hingston | 5 | 700 | 62.33 |