Fitness landscape-based characterisation of nature-inspired algorithms

Matthew Crossley, Andy Nisbet, Martyn Amos

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

1 Citation (Scopus)

Abstract

A significant challenge in nature-inspired algorithmics is the identification of specific characteristics of problems that make them harder (or easier) to solve using specific methods. The hope is that, by identifying these characteristics, we may more easily predict which algorithms are best-suited to problems sharing certain features. Here, we approach this problem using fitness landscape analysis. Techniques already exist for measuring the "difficulty" of specific landscapes, but these are often designed solely with evolutionary algorithms in mind, and are generally specific to discrete optimisation. In this paper we develop an approach for comparing a wide range of continuous optimisation algorithms. Using a fitness landscape generation technique, we compare six different nature-inspired algorithms and identify which methods perform best on landscapes exhibiting specific features.

Original languageEnglish
Title of host publicationAdaptive and Natural Computing Algorithms - 11th International Conference, ICANNGA 2013, Proceedings
PublisherSpringer
Pages110-119
Number of pages10
ISBN (Electronic)9783642372131
ISBN (Print)9783642372124
DOIs
Publication statusPublished - Dec 2013
Externally publishedYes
Event11th International Conference on Adaptive and Natural Computing Algorithms, ICANNGA 2013 - Lausanne, Switzerland
Duration: 4 Apr 20136 Apr 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7824 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference11th International Conference on Adaptive and Natural Computing Algorithms, ICANNGA 2013
CountrySwitzerland
CityLausanne
Period4/04/136/04/13

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