• DOI:10.1007/978-3-642-37213-1_12
• Corpus ID: 16148037

Fitness Landscape-Based Characterisation of Nature-Inspired Algorithms

@inproceedings{Crossley2012FitnessLC,
  title={Fitness Landscape-Based Characterisation of Nature-Inspired Algorithms},
  author={Matthew Crossley and Andy Nisbet and Martyn Amos},
  booktitle={International Conference on Adaptive and Natural Computing Algorithms},
  year={2012}
}

• M. Crossley, A. Nisbet, M. Amos
• Published in
  International Conference on…
  11 October 2012
• Computer Science

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.