Dynamic selection of evolutionary algorithm operators based on online learning and fitness landscape metrics

Pietro A. CONSOLI*, Leandro L. MINKU, Xin YAO

*Corresponding author for this work

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

15 Citations (Scopus)


Self-adaptive mechanisms for the identification of the most suitable variation operator in Evolutionary meta-heuristics rely almost exclusively on the measurement of the fitness of the offspring, which may not be sufficient to assess the optimality of an operator (e.g., in a landscape with an high degree of neutrality). This paper proposes a novel Adaptive Operator Selection mechanism which uses a set of four Fitness Landscape Analysis techniques and an online learning algorithm, Dynamic Weighted Majority, to provide more detailed informations about the search space in order to better determine the most suitable crossover operator on a set of Capacitated Arc Routing Problem (CARP) instances. Extensive comparison with a state of the art approach has proved that this technique is able to produce comparable results on the set of benchmark problems.

Original languageEnglish
Title of host publicationSimulated Evolution and Learning: 10th International Conference, SEAL 2014, Dunedin, New Zealand, December 15-18, Proceedings
EditorsGrant DICK, Will N. BROWNE, Peter WHIGHAM, Mengjie ZHANG, Thu Bui LAM, Hisao ISHIBUCHI, Yaochu JIN, Xiaodong LI, Yuhui SHI, Pramod SINGH, Kay Chen TAN, Ke TANG
PublisherSpringer, Cham
Number of pages12
ISBN (Electronic)9783319135632
ISBN (Print)9783319135625
Publication statusPublished - 2014
Externally publishedYes

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer, Cham
ISSN (Print)0302-9743

Bibliographical note

Publisher Copyright:
© Springer International Publishing Switzerland 2014.


  • Crossover Operator
  • Memetic Algorithm
  • Concept Drift
  • Dynamic Selection
  • Credit Assignment


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