A framework for knowledge integrated evolutionary algorithms

A. Hallawa, A. Yaman, G. Iacca, G. Ascheid

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

2 Citations (Scopus)
5 Downloads (Pure)

Abstract

One of the main reasons for the success of Evolutionary Algorithms (EAs) is their general-purposeness, i.e. the fact that they can be applied in a straight forward manner to a broad range of optimization problems, without any specific prior knowledge. On the other hand, it has been shown that incorporating a priori knowledge, such as expert knowledge or empirical findings, can significantly improve the performance of an EA. However, integrating knowledge in EAs poses numerous challenges. It is often the case that the features of the search space are unknown, hence any knowledge associated with the search space properties can be hardly used. In addition, a priori knowledge is typically problem-specific and hard to generalize. In this paper, we propose a framework, called Knowledge Integrated Evolutionary Algorithm (KIEA), which facilitates the integration of existing knowledge into EAs. Notably, the KIEA framework is EA-agnostic, i.e. it works with any evolutionary algorithm, problem-independent, i.e. it is not dedicated to a specific type of problems and expandable, i.e. its knowledge base can grow over time. Furthermore, the framework integrates knowledge while the EA is running, thus optimizing the consumption of computational power. In the preliminary experiments shown here, we observe that the KIEA framework produces in the worst case an 80% improvement on the converge time, w.r.t. the corresponding “knowledge-free” EA counterpart.

Original languageEnglish
Title of host publicationApplications of Evolutionary Computation
Subtitle of host publication20th European Conference, EvoApplications 2017, Amsterdam, The Netherlands, April 19-21, 2017, Proceedings, Part I
Place of PublicationDordrecht
PublisherSpringer
Pages653-669
Number of pages7
ISBN (Electronic)978-3-319-55849-3
ISBN (Print)978-3-319-55848-6
DOIs
Publication statusPublished - 2017
Event20th European Conference on the Applications of Evolutionary Computation (EvoApplications 2017), April 19-21, 2017, Amsterdam, The Netherlands - Amsterdam, Netherlands
Duration: 19 Apr 201721 Apr 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10199 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference20th European Conference on the Applications of Evolutionary Computation (EvoApplications 2017), April 19-21, 2017, Amsterdam, The Netherlands
Abbreviated titleEvoApplications 2017
CountryNetherlands
City Amsterdam
Period19/04/1721/04/17

Keywords

  • Evolutionary algorithm fingerprint
  • Evolutionary algorithms
  • Knowledge incorporation
  • Landscape analysis

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