A pre-initialization stage of population-based bio-inspired metaheuristics for handling expensive optimization problems

Research output: Chapter in Book/Report/Conference proceedingConference proceeding

1 Citation (Scopus)

Abstract

Metaheuristics are probabilistic optimization algorithms which are applicable to a wide range of optimization problems. Bio-inspired, also called nature-inspired, optimization algorithms are the most widely-known metaheuristics. The general scheme of bio-inspired algorithms consists in an initial stage of randomly generated solutions which evolve through search operations, for several generations, towards an optimal value of the fitness function of the optimization problem at hand. Such a scenario requires repeated evaluation of the fitness function. While in some applications each evaluation will not take more than a fraction of a second, in others, mainly those encountered in data mining, each evaluation may take up several minutes, hours, or even more. This category of optimization problems is called expensive optimization. Such cases require a certain modification of the above scheme. In this paper we present a new method for handling expensive optimization problems. This method can be applied with different population-based bio-inspired optimization algorithms. Although the proposed method is independent of the application to which it is applied, we experiment it on a data mining task.

Original languageEnglish
Title of host publicationAdvanced Data Mining and Applications - 9th International Conference, ADMA 2013, Proceedings
EditorsHiroshi Motoda, Zhaohui Wu, Longbing Cao, Osmar Zaiane, Min Yao, Wei Wang
PublisherSpringer
Pages396-403
Number of pages8
EditionPART 2
ISBN (Print)9783642539169
DOIs
Publication statusPublished - 1 Dec 2013
Externally publishedYes
Event9th International Conference on Advanced Data Mining and Applications, ADMA 2013 - Hangzhou, China
Duration: 14 Dec 201316 Dec 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume8347 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference9th International Conference on Advanced Data Mining and Applications, ADMA 2013
CountryChina
CityHangzhou
Period14/12/1316/12/13

Keywords

  • Bio-inspired Optimization
  • Differential Evolution
  • Expensive Optimization
  • Genetic Algorithms
  • Metaheuristics
  • Optimization Applications in Data Mining

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'A pre-initialization stage of population-based bio-inspired metaheuristics for handling expensive optimization problems'. Together they form a unique fingerprint.

  • Cite this

    Fuad, M. M. M. (2013). A pre-initialization stage of population-based bio-inspired metaheuristics for handling expensive optimization problems. In H. Motoda, Z. Wu, L. Cao, O. Zaiane, M. Yao, & W. Wang (Eds.), Advanced Data Mining and Applications - 9th International Conference, ADMA 2013, Proceedings (PART 2 ed., pp. 396-403). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8347 LNAI, No. PART 2). Springer. https://doi.org/10.1007/978-3-642-53917-6_35