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

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Colleges, School and Institutes


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
Pages (from-to)359-370
Number of pages12
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publication statusPublished - 2014
Event10th Asia-Pacific conference on simulated Evolution and Learning SEAL 2014 - Dunedin, New Zealand
Duration: 15 Dec 201418 Dec 2014


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