Name
Abstract | ||
|---|---|---|
Non-elitist evolutionary algorithms (EAs) can be beneficial in optimisation of noisy and or rugged fitness landscapes. However, this benefit can only be realised if the parameters of the non-elitist EAs are carefully adjusted in accordance with the fitness function. Self-adaptation is a promising parameter adaptation method that encodes and evolves parameters in the chromosome. Existing self-adaptive EAs often sort the population by first preferring higher fitness and then the mutation rate. A previous study (Case and Lehre, 2020) proved that self-adaptation can be effective in certain discrete problems with unknown structure. However, the population can be trapped on local optima, because individuals in “dense” fitness valleys which survive high mutation rates and individuals on “sparse” local optima which only survive with lower mutation rates cannot be simultaneously preserved. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1007/978-3-031-14714-2_22 | Parallel Problem Solving from Nature |
Keywords | DocType | Citations |
Evolutionary algorithms,Self-adaptation,Local optima,Combinatorial optimisation,Noisy optimisation | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qin Xiaoyu | 1 | 0 | 0.34 |
| Per Kristian Lehre | 2 | 627 | 42.60 |