Integrating Exploratory Landscape Analysis into Metaheuristic Algorithms

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1 Citation (Scopus)


The no free lunch (NFL) theorem puts a limit to the range of problems a certain metaheuristic algorithm can be applied to successfully. For many methods these limits are unknown a priori and have to be discovered by experimentation. With the use of fitness landscape analysis (FLA) it is possible to obtain characteristic data and understand why methods perform better than others. In past research this data has been gathered mostly by a separate set of exploration algorithms. In this work it is studied how FLA methods can be integrated into the metaheuristic algorithm. We present a new exploratory method for obtaining landscape features that is based on path relinking (PR) and show that this characteristic information can be obtained faster than with traditional sampling methods. Path relinking is used in several metaheuristic which creates the possibility of integrating these features and enhance algorithms to output landscape analysis in addition to good solutions.

Original languageEnglish
Title of host publicationComputer Aided Systems Theory – EUROCAST 2017 - 16th International Conference, Revised Selected Papers
EditorsRoberto Moreno-Diaz, Alexis Quesada-Arencibia, Franz Pichler
Number of pages8
Publication statusPublished - 2018
EventInternational Conference Computer Aided Systems Theory EUROCAST 2017 - Las Palmas de Gran Canaria, Spain
Duration: 19 Feb 201724 Feb 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10671 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceInternational Conference Computer Aided Systems Theory EUROCAST 2017
CityLas Palmas de Gran Canaria
Internet address


  • no free lunch
  • fitness landscape
  • heuristic algorithms


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