Name
Abstract | ||
|---|---|---|
The influence of non-constant population sizes in evolutionary multi-objective optimization algorithms is investigated. In contrast to evolutionary single-objective optimization algorithms an increasing population size is considered beneficial when approaching the Pareto-front. Firstly, different deterministic schedules are tested, featuring different parameters like the initial population size. Secondly, a simple adaptation method is proposed. Considering all results, an increasing population size during an evolutionary multi-objective optimization algorithm run saves fitness function evaluations compared to a fixed population size. In particular, the results obtained with the adaptive method are most promising. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-10762-2_57 | PARALLEL PROBLEM SOLVING FROM NATURE - PPSN XIII |
Field | DocType | Volume |
Population,Mathematical optimization,Adaptive method,Computer science,Evolutionary computation,Multi-objective optimization,Fitness function,Population size,Schedule,Optimization algorithm | Conference | 8672 |
ISSN | Citations | PageRank |
0302-9743 | 2 | 0.38 |
References | Authors | |
10 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tobias Glasmachers | 1 | 321 | 47.49 |
| Boris Naujoks | 2 | 704 | 47.78 |
| Günter Rudolph | 3 | 219 | 48.59 |