J. Peter and . Angeline, Adaptive and self-adaptive evolutionary computations, Computational Intelligence: A Dynamic Systems Perspective, pp.152-163, 1995.

T. Bäck, The interaction of mutation rate, selection, and self-adaptation within a genetic algorithm, Parallel Problem Solving from Nature 2 (Proc. 2nd Int. Conf. on Parallel Problem Solving from Nature, pp.85-94, 1992.

T. Bäck and M. Schütz, Intelligent mutation rate control in canonical genetic algorithms, International Syposium on Methodologies for Intelligent Systems, pp.158-167, 1996.
DOI : 10.1007/3-540-61286-6_141

F. Didierjean, AgCSP, une bibliothèque de classes C++ pour le développement d'opérateurs génétiques pour CSP, 2002.

A. E. Eiben, R. Hinterding, and Z. Michalewicz, Parameter control in evolutionary algorithms, IEEE Transactions on Evolutionary Computation, vol.3, issue.2, p.124, 1999.
DOI : 10.1109/4235.771166

URL : https://hal.archives-ouvertes.fr/inria-00140549

A. E. Eiben, P. Raue, and Z. Ruttkay, GA-easy and GA-hard constraint satisfaction problems, Proc. of European Conferance on Artificial Intelligence, Workshop on Constraint Processing, pp.267-283, 1994.
DOI : 10.1007/3-540-59479-5_30

A. E. Eiben and J. K. Van-der-hauw, Adaptive penalties for evolutionary graph coloring, Artificial Evolution ? Third European Conference, pp.95-106, 1997.
DOI : 10.1007/BFb0026593

T. C. Fogarty, Varying the probability of mutation in the genetic algorithm, Proceedings of the Third International Conference on Genetic Algorithms, pp.104-109, 1989.

P. Galinier and J. K. Hao, Hybrid evolutionary algorithms for graph coloring, Journal of Combinatorial Optimization, vol.3, issue.4, pp.379-397, 1999.
DOI : 10.1023/A:1009823419804

J. Hesser and R. Manner, Towards an optimal mutation probability for genetic algorithms, Parallel Problem Solving from Nature, pp.23-32, 1990.
DOI : 10.1007/BFb0029727

F. T. Lin, C. Y. Kao, and C. C. Hsu, Applying the genetic approach to simulated annealing in solving some NP-hard problems, IEEE Transactions on Systems, Man, and Cybernetics, vol.23, issue.6, pp.1752-1767

H. Mühlenbein, How genetic algorithms really work: I. mutation and hill-climbing, Parallel Problem Solving from Nature 2, pp.15-25, 1992.

J. Nicholas, F. A. Radcliffe, and . George, A study in set recombination, Proceedings of the Fifth International Conference on Genetic Algorithms, pp.23-30, 1993.

J. Nicholas, P. D. Radcliffe, and . Surry, Fundamental limitations on search algorithms: Evolutionary computing in perspective, Computer Science Today, pp.275-291, 1995.

M. Riff-rojas, A network-based adaptive evolutionary algorithm for constraint satisfaction problems, Meta-heuristics : Advances and Trends in Local Search Paradigms for Optimization, pp.269-283, 1998.

J. E. Smith and T. C. Fogarty, Adaptively parameterised evolutionary systems: Self adaptive recombination and mutation in a genetic algorithm, Parallel Problem Solving from Nature IV, pp.441-450, 1996.
DOI : 10.1007/3-540-61723-X_1008

G. Syswerda, Uniform crossover in genetic algorithms, Proceeding of Third International Conference on Genetic Algorithms and Their Applications, pp.2-9, 1989.

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