Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
New methods to color the vertices of a graph
Communications of the ACM
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
On Replacement Strategies in Steady State Evolutionary Algorithms
Evolutionary Computation
Where the really hard problems are
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
Solving constraint satisfaction problems using hybrid evolutionarysearch
IEEE Transactions on Evolutionary Computation
ISMIS'08 Proceedings of the 17th international conference on Foundations of intelligent systems
On-the-fly calibrating strategies for evolutionary algorithms
Information Sciences: an International Journal
C-strategy: a dynamic adaptive strategy for the CLONALG algorithm
Transactions on computational science VIII
C-strategy: a dynamic adaptive strategy for the CLONALG algorithm
Transactions on computational science VIII
Using self-adaptable probes for dynamic parameter control of parallel evolutionary algorithms
ISMIS'05 Proceedings of the 15th international conference on Foundations of Intelligent Systems
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Our research has been focused on developing techniques for solving binary constraint satisfaction problems (CSP) using evolutionary algorithms, which take into account the constraint graphs topology. In this paper, we introduce a new idea to improve the performance of evolutionary algorithms, that solve complex problems. It is inspired from a real world observation: The ability to evolve for an individual in an environment that changes is not only related to its genetic material. It also comes from what has learned from it parents. The key idea of this paper is to use its inheritance to dynamically improve the way the algorithm creates a new population using a given set of operators. This new dynamic operator selection strategy has been applied to an evolutionary algorithm to solve CSPs, but can be easily extended to other class of evolutionary algorithms. A set of benchmarks shows how the new strategy can help to solve large NP-hard problems with the 3-graph coloring example.