Fitness Landscape-Based Characterisation of Nature-Inspired AlgorithmsReport as inadecuate



 Fitness Landscape-Based Characterisation of Nature-Inspired Algorithms


Fitness Landscape-Based Characterisation of Nature-Inspired Algorithms - Download this document for free, or read online. Document in PDF available to download.

Download or read this book online for free in PDF: Fitness Landscape-Based Characterisation of Nature-Inspired Algorithms
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.



Author: Matthew Crossley; Andy Nisbet; Martyn Amos

Source: https://archive.org/







Related documents