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

Stoean C., Preuss M., Stoean R.


Abstract
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.

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



Publication type
Conference Paper

Peer reviewed
No

Publication status
Published

Year
2013

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

Venue
Amsterdam, nld

Start page
1063

End page
1070

Pages range
1063-1070

Volume
null

Language
English

ISBN
9781450319645

DOI

Full text

Affiliation
Universitatea din Craiova; Universitat Dortmund