Numerical recipes: the art of scientific computing
Numerical recipes: the art of scientific computing
Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Niching methods for genetic algorithms
Niching methods for genetic algorithms
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Handbook of Evolutionary Computation
Handbook of Evolutionary Computation
Self-Organizing Maps
On the analysis of the (1+ 1) evolutionary algorithm
Theoretical Computer Science
Genetic Algorithms for Tracking Changing Environments
Proceedings of the 5th International Conference on Genetic Algorithms
Heuristic Crossovers for Real-Coded Genetic Algorithms Based on Fuzzy Connectives
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
A Statistical Mechanical Formulation of the Dynamics of Genetic Algorithms
Selected Papers from AISB Workshop on Evolutionary Computing
General schema theory for genetic programming with subtree-swapping crossover: part I
Evolutionary Computation
Genetic algorithms as function optimizers
Genetic algorithms as function optimizers
On classes of functions for which No Free Lunch results hold
Information Processing Letters
General schema theory for genetic programming with subtree-swapping crossover: Part II
Evolutionary Computation
Genetic Programming and Evolvable Machines
Real royal road functions for constant population size
Theoretical Computer Science
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
An Analysis of Two-Parent Recombinations for Real-Valued Chromosomes in an Infinite Population
Evolutionary Computation
A Study of Fitness Distance Correlation as a Difficulty Measure in Genetic Programming
Evolutionary Computation
On the Choice of the Offspring Population Size in Evolutionary Algorithms
Evolutionary Computation
Runtime Analysis of the (μ+1) EA on Simple Pseudo-Boolean Functions
Evolutionary Computation
Some results about the Markov chains associated to GPs and general EAs
Theoretical Computer Science - Foundations of genetic algorithms
Randomized local search, evolutionary algorithms, and the minimum spanning tree problem
Theoretical Computer Science
Fitness-proportional negative slope coefficient as a hardness measure for genetic algorithms
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A markov chain framework for the simple genetic algorithm
Evolutionary Computation
Schemata evolution and building blocks
Evolutionary Computation
Free lunches for function and program induction
Proceedings of the tenth ACM SIGEVO workshop on Foundations of genetic algorithms
Practical model of genetic programming's performance on rational symbolic regression problems
EuroGP'08 Proceedings of the 11th European conference on Genetic programming
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Bandit problems and the exploration/exploitation tradeoff
IEEE Transactions on Evolutionary Computation
Coarse-Grained Dynamics for Generalized Recombination
IEEE Transactions on Evolutionary Computation
An empirical tool for analysing the collective behaviour of population-based algorithms
EvoApplications'12 Proceedings of the 2012t European conference on Applications of Evolutionary Computation
On Evolutionary Exploration and Exploitation
Fundamenta Informaticae
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Several previous studies have focused on modelling and analysing the collective dynamic behaviour of population-based algorithms. However, an empirical approach for identifying and characterising such a behaviour is surprisingly lacking. In this paper, we present a new model to capture this collective behaviour, and to extract and quantify features associated with it. The proposed model studies the topological distribution of an algorithm's activity from both a genotypic and a phenotypic perspective, and represents population dynamics using multiple levels of abstraction. The model can have different instantiations. Here it has been implemented using a modified version of self-organising maps. These are used to represent and track the population motion in the fitness landscape as the algorithm operates on solving a problem. Based on this model, we developed a set of features that characterise the population's collective dynamic behaviour. By analysing them and revealing their dependency on fitness distributions, we were then able to define an indicator of the exploitation behaviour of an algorithm. This is an entropy-based measure that assesses the dependency on fitness distributions of different features of population dynamics. To test the proposed measures, evolutionary algorithms with different crossover operators, selection pressure levels and population handling techniques have been examined, which lead populations to exhibit a wide range of exploitation-exploration behaviours.