Name
Title | ||
|---|---|---|
Offspring population size matters when comparing evolutionary algorithms with self-adjusting mutation rates |
Abstract | ||
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ABSTRACTWe analyze the performance of the 2-rate (1 + λ) Evolutionary Algorithm (EA) with self-adjusting mutation rate control, its 3-rate counterpart, and a (1 + λ) EA variant using multiplicative update rules on the OneMax problem. We compare their efficiency for offspring population sizes ranging up to λ = 3, 200 and problem sizes up to n = 100,000. Our empirical results show that the ranking of the algorithms is very consistent across all tested dimensions, but strongly depends on the population size. While for small values of λ the 2-rate EA performs best, the multiplicative updates become superior for starting for some threshold value of λ between 50 and 100. Interestingly, for population sizes around 50, the (1 + λ) EA with static mutation rates performs on par with the best of the self-adjusting algorithms. We also consider how the lower bound pmin for the mutation rate influences the efficiency of the algorithms. We observe that for the 2-rate EA and the EA with multiplicative update rules the more generous bound pmin = 1/n2 gives better results than pmin = 1/n when λ is small. For both algorithms the situation reverses for large λ. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3321707.3321827 | Genetic and Evolutionary Computation Conference |
Field | DocType | Volume |
Population,Mathematical optimization,Combinatorics,Mutation rate,Multiplicative function,Evolutionary algorithm,Computer science,Upper and lower bounds,Population size,Lambda | Journal | abs/1904.08032 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Anna Rodionova | 1 | 0 | 0.34 |
| Kirill Antonov | 2 | 0 | 0.68 |
| Arina Buzdalova | 3 | 61 | 9.42 |
| Carola Doerr | 4 | 259 | 34.91 |