Sequential parameter optimization

Author

Bartz-Beielstein, Thomas ; Lasarczyk, Christian W G ; Preuss, Mike

Author_Institution

Dept. of Comput. Sci., Dortmund Univ.

Volume

1

fYear

2005

fDate

5-5 Sept. 2005

Firstpage

773

Abstract

Sequential parameter optimization is a heuristic that combines classical and modern statistical techniques to improve the performance of search algorithms. To demonstrate its flexibility, three scenarios are discussed: (1) no experience how to choose the parameter setting of an algorithm is available, (2) a comparison with other algorithms is needed, and (3) an optimization algorithm has to be applied effectively and efficiently to a complex real-world optimization problem. Although sequential parameter optimization relies on enhanced statistical techniques such as design and analysis of computer experiments, it can be performed algorithmically and requires basically the specification of the relevant algorithm´s parameters

Keywords

heuristic programming; optimisation; search problems; statistical analysis; optimization algorithm; search algorithm; sequential parameter optimization; statistical techniques; Algorithm design and analysis; Computer science; Design for experiments; Design optimization; Evolutionary computation; Genetic algorithms; Modems; Performance analysis; Statistics; Stochastic processes;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation, 2005. The 2005 IEEE Congress on

Conference_Location

Edinburgh, Scotland

Print_ISBN

0-7803-9363-5

Type

conf

DOI

10.1109/CEC.2005.1554761

Filename

1554761