Stochastic reverse hill climbing and iterated local search

Author

Cotta, Carlos ; Alba, Enrique ; Troya, José M.

Author_Institution

Dept. Lenguajes y Ciencias de la Comput., Malaga Univ., Spain

Volume

2

fYear

1999

fDate

1999

Abstract

This paper analyzes the detection of stagnation states in iterated local search algorithms. This is done considering elements such as the population size, the length of the encoding and the number of observed non-improving iterations. This analysis isolates the features of the target problem within one parameter for which three different estimations are given: two static a priori estimations and a dynamic approach. In the latter case, a stochastic reverse hill climbing algorithm is used to extract information from the fitness landscape. The applicability of these estimations is studied and exemplified on different problems

Keywords

evolutionary computation; parameter estimation; search problems; stochastic processes; dynamic approach; encoding length; fitness landscape; iterated local search algorithms; nonimproving iterations; population size; stagnation state detection; static a priori estimations; stochastic reverse hill climbing; Algorithm design and analysis; Data mining; Encoding; Erbium; Genetic mutations; Parameter estimation; Probability; Stochastic processes;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress on

Conference_Location

Washington, DC

Print_ISBN

0-7803-5536-9

Type

conf

DOI

10.1109/CEC.1999.782669

Filename

782669