A generator for multimodal test functions with multiple global optima

Ronkkonen, J, Li, X, Kyrki, V and Lampinen, J 2008, 'A generator for multimodal test functions with multiple global optima', in X Li et al. (ed.) Proceedings of the 7th International Conference on Simulated Evolution and Learning (SEAL 08), Melbourne, Australia, 7-10 December 2008.


Document type: Conference Paper
Collection: Conference Papers

Title A generator for multimodal test functions with multiple global optima
Author(s) Ronkkonen, J
Li, X
Kyrki, V
Lampinen, J
Year 2008
Conference name The 7th International Conference on Simulated Evolution and Learning (SEAL 08)
Conference location Melbourne, Australia
Conference dates 7-10 December 2008
Proceedings title Proceedings of the 7th International Conference on Simulated Evolution and Learning (SEAL 08)
Editor(s) X Li et al.
Publisher Springer
Place of publication Berlin, Germany
Abstract The topic of multimodal function optimization, where the aim is to locate more than one solution, has attracted a growing interest especially in the evolutionary computing research community. To experimentally evaluate the strengths and weaknesses of multimodal optimization algorithms, it is important to use test functions representing different characteristics and of various levels of difficulty. However, the available selection of multimodal test problems with multiple global optima is rather limited at the moment and no general framework exists. This paper describes our attempt in constructing a test function generator to allow the generation of easily tunable test functions. The aim is to provide a general and easily expandable environment for testing different methods of multimodal optimization. Several function families with different characteristics are included. The generator implements new parameterizable function families for generating desired landscapes and a selection of well known test functions from literature, which can be rotated and stretched. The module can be easily imported to any optimization algorithm implementation compatible with C programming language.
Subjects Neural, Evolutionary and Fuzzy Computation
Keyword(s) multimodal optimization
test function generator
global optimization
DOI - identifier 10.1007/978-3-540-89694-4_25
Copyright notice © 2008 Springer-Verlag Berlin Heidelberg
Versions
Version Filter Type
Altmetric details:
Access Statistics: 184 Abstract Views  -  Detailed Statistics
Created: Mon, 04 Jan 2010, 08:16:50 EST by Catalyst Administrator
© 2014 RMIT Research Repository • Powered by Fez SoftwareContact us