Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Memetic algorithms: a short introduction
New ideas in optimization
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Evolutionary Algorithms: The Role of Mutation and Recombination
Evolutionary Algorithms: The Role of Mutation and Recombination
Migration Policies, Selection Pressure, and Parallel Evolutionary Algorithms
Journal of Heuristics
Distributed Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Diversity-Guided Evolutionary Algorithms
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Dynamic Programming and Strong Bounds for the 0-1 Knapsack Problem
Management Science
The influence of migration sizes and intervals on island models
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
The speciating island model: an alternative parallel evolutionary algorithm
Journal of Parallel and Distributed Computing - Special issue on parallel bioinspired algorithms
Costs and Benefits of Tuning Parameters of Evolutionary Algorithms
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Genotypic differences and migration policies in an island model
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
On the impact of the migration topology on the Island Model
Parallel Computing
Autonomous operator management for evolutionary algorithms
Journal of Heuristics
On mutation and crossover in the theory of evolutionary algorithms
On mutation and crossover in the theory of evolutionary algorithms
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
A dynamic island model for adaptive operator selection
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Autonomous local search algorithms with island representation
LION'12 Proceedings of the 6th international conference on Learning and Intelligent Optimization
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
General framework for localised multi-objective evolutionary algorithms
Information Sciences: an International Journal
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This work presents a dynamic island model framework for helping the resolution of combinatorial optimization problems with evolutionary algorithms. In this framework, the possible migrations among islands are represented by a complete graph. The migrations probabilities associated to each edge are dynamically updated with respect to the last migrations impact. This new framework is tested on the well-known 0/1 Knapsack problem and MAX-SAT problem. Good results are obtained and several properties of this framework are studied.