EA-based parameter tuning of multimodal optimization performance by means of different surrogate models

Stoean C., Preuss M., Stoean R.


Zusammenfassung
In the current study, parameter tuning is performed for two evolutionary optimization techniques, Covariance Matrix Adaptation Evolution Strategy and Topological Species Conservation. They are applied for three multimodal benchmark functions with various properties and several outputs are considered. A data set with input parameters and metaheuristic outcomes is used for training four surrogate models. They are then each used by a genetic algorithm that is employed for searching the best parameter settings for the initial approaches. The genetic algorithm uses the model outputs as the direct fitness evaluation and only the best found parameter setting is tested within the original metaheuristics. Each model quality is priory evaluated, but they are all subsequently used in the search process to observe how the (in)accuracy influences the final result. Additionally, the genetic algorithm is used for tuning these approaches directly to test if the search conducts to the same parameter set, or at least close to it.

Schlüsselwörter
Evolutionary algorithms; Function optimization; Parameter tuning; Surrogate modeling



Publikationstyp
Aufsatz (Konferenz)

Begutachtet
Nein

Publikationsstatus
Veröffentlicht

Jahr
2013

Konferenz
15th Annual Conference on Genetic and Evolutionary Computation, GECCO 2013

Konferenzort
Amsterdam, nld

Erste Seite
1063

Letzte Seite
1070

Seiten
1063-1070

Band
null

Sprache
Englisch

ISBN
9781450319645

DOI

Gesamter Text

Affiliierungen
Universitatea din Craiova; Universitat Dortmund