Fitness Landscape-Based Characterisation of Nature-Inspired Algorithms

Crossley, Matthew, Nisbet, Andy and Amos, Martyn (2013) Fitness Landscape-Based Characterisation of Nature-Inspired Algorithms. In: Adaptive and Natural Computing Algorithms. Lecture Notes in Computer Science, 7824 . Springer, pp. 110-119. ISBN 978-3-642-37212-4

Full text not available from this repository.
Official URL: http://dx.doi.org/10.1007/978-3-642-37213-1_12

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.

Item Type: Book Section
Subjects: G900 Others in Mathematical and Computing Sciences
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Becky Skoyles
Date Deposited: 20 Feb 2019 09:02
Last Modified: 20 Feb 2019 09:02
URI: http://nrl.northumbria.ac.uk/id/eprint/38127

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