TY - GEN
T1 - Fitness landscape-based characterisation of nature-inspired algorithms
AU - Crossley, Matthew
AU - Nisbet, Andy
AU - Amos, Martyn
PY - 2013/12
Y1 - 2013/12
N2 - 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.
AB - 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.
U2 - 10.1007/978-3-642-37213-1_12
DO - 10.1007/978-3-642-37213-1_12
M3 - Conference contribution
AN - SCOPUS:84893516144
SN - 9783642372124
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 110
EP - 119
BT - Adaptive and Natural Computing Algorithms - 11th International Conference, ICANNGA 2013, Proceedings
PB - Springer
T2 - 11th International Conference on Adaptive and Natural Computing Algorithms, ICANNGA 2013
Y2 - 4 April 2013 through 6 April 2013
ER -