On evolutionary exploration and exploitation
Fundamenta Informaticae
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Self-Organizing Maps
Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
A Statistical Mechanical Formulation of the Dynamics of Genetic Algorithms
Selected Papers from AISB Workshop on Evolutionary Computing
Genetic algorithms as function optimizers
Genetic algorithms as function optimizers
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Fitness-proportional negative slope coefficient as a hardness measure for genetic algorithms
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Analysis of exploration and exploitation in evolutionary algorithms by ancestry trees
International Journal of Innovative Computing and Applications
Coarse-Grained Dynamics for Generalized Recombination
IEEE Transactions on Evolutionary Computation
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Understanding the emergent collective behaviour (and the properties associated with it) of population-based algorithms is an important prerequisite for making technically sound choices of algorithms and also for designing new algorithms for specific applications. In this paper, we present an empirical approach to analyse and quantify the collective emergent behaviour of populations. In particular, our long term objective is to understand and characterise the notions of exploration and exploitation and to make it possible to characterise and compare algorithms based on such notions. The proposed approach uses self-organising maps as a tool to track the population dynamics and extract features that describe a population "functionality" and "structure".