Fast algorithms for mining association rules, Proceeding 20th International Conference Very Large Data Bases, VLDB, pp.487-499 ,
Population based incremental learning. Techni- cal Report CMU-CS-94-163, 1994. ,
Gene trajectory clustering with a hybrid genetic algorithm and expectation maximization method, IEEE International Joint Conference on Neural Networks, pp.1669-1674, 2004. ,
On improving evolutionary algorithms by using data mining for the oil collector vehicle routing problem. International Network Optimization Conference, 2003. ,
A New Representation and Operators for Genetic Algorithms Applied to Grouping Problems, Evolutionary Computation, vol.2, issue.2, pp.123-144, 1994. ,
DOI : 10.1162/evco.1994.2.2.123
A hybrid multi-objective evolutionary algorithm using an inverse neural network, Hybrid Metaheuristic, pp.25-30, 2004. ,
Clustering with a genetically optimized approach, IEEE Transactions on Evolutionary Computation, vol.3, issue.2, pp.103-112, 1999. ,
DOI : 10.1109/4235.771164
Coevolutionary genetic algorithm with effective exploration and exploitation of useful schemata, Proceedings of the International Conference on Neural Information Systems, pp.424-427, 1997. ,
A NOVEL HYBRID FRAMEWORK OF COEVOLUTIONARY GA AND MACHINE LEARNING, International Journal of Computational Intelligence and Applications, vol.02, issue.01, 2002. ,
DOI : 10.1142/S1469026802000415
Fusion of coevolutionary ga and machine learning techniques through effective schema extraction, Proceedings of the Genetic and Evolutionary Computation Conference, pp.764-771, 2001. ,
Improvements to the scalability of multiobjective clustering, 2005 IEEE Congress on Evolutionary Computation, pp.438-445, 2005. ,
DOI : 10.1109/CEC.2005.1554990
Simultaneously applying multiple mutation operators in genetic algorithms, Journal of Heuristics, vol.6, issue.4, pp.439-455, 2000. ,
DOI : 10.1023/A:1009642825198
Extraction de connaissances pertinentes sur le comportement des systemes de production : une approche conjointe par optimisation evolutionniste via simulation et apprentissage, 2004. ,
Configuration and analysis of a multiproduct kanban system using evolutionary optimisation coupled to machine learning, Proceedings of CESA 2003, the IMACS Multiconference Computational Engineering in Systems Applications, 2003. ,
A comprehensive survey of fitness approximation in evolutionary computation, Soft Computing, vol.9, issue.1, pp.3-12, 2005. ,
DOI : 10.1007/s00500-003-0328-5
Reducing Fitness Evaluations Using Clustering Techniques and Neural Network Ensembles, Genetic and Evolutionary Computation Conference, pp.688-699, 2004. ,
DOI : 10.1007/978-3-540-24854-5_71
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.466.9186
Preliminary Investigation of the ???Learnable Evolution Model??? for Faster/Better Multiobjective Water Systems Design, Third International Conference on Evolutionary Multi-Criterion Optimization (EMO'05), pp.841-855, 2005. ,
DOI : 10.1007/978-3-540-31880-4_58
URL : https://hal.archives-ouvertes.fr/inria-00001170
An efficient genetic algorithms with less fitness evaluation by clustering, Proceedings of IEEE Congress on Evolutionary Computation, pp.887-894, 2001. ,
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation, 2002. ,
DOI : 10.1007/978-1-4615-1539-5
Genetic learning from experience, The 2003 Congress on Evolutionary Computation, 2003. CEC '03., pp.2118-2125, 2003. ,
DOI : 10.1109/CEC.2003.1299934
Learning for evolutionary design, NASA/DoD Conference on Evolvable Hardware, 2003. Proceedings., pp.17-23, 2003. ,
DOI : 10.1109/EH.2003.1217637
Learnable evolution model: Evolutionary processes guided by machine learning, Machine Learning, pp.9-40, 2000. ,
Speeding Up Evolution through Learning: LEM, Intelligent Information Systems, pp.243-256, 2000. ,
DOI : 10.1007/978-3-7908-1846-8_22
Selection of most representative training examples and incremental generation of vl1 hypothesis: The underlying methodology and the descriptions of programs esel and aq11, 1978. ,
The multipurpose incremental learning system aq15 and its testing application to three medical domains, Proc. of the Fifth National Conference on Artificial Intelligence, pp.1041-1045, 1986. ,
From recombination of genes to the estimation of distributions I. Binary parameters, Lecture Notes in Computer Science, vol.1141, pp.178-187, 1996. ,
DOI : 10.1007/3-540-61723-X_982
BOA: The Bayesian optimization algorithm, Proceedings of the Genetic and Evolutionary Computation Conference GECCO-99, pp.525-532, 1999. ,
Case-based initialization of genetic algorithms, Fifth International Conference on Genetic Algorithms, pp.84-91, 1993. ,
An incremental-approximate-clustering approach for developing dynamic reduced models for design optimization, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512), pp.986-993, 2000. ,
DOI : 10.1109/CEC.2000.870752
Using case based learning to improve genetic algorithm based design optimization, Proceedings of the Seventh International Conference on Genetic Algorithms (ICGA97), 1997. ,
Informed operators: Speeding up genetic-algorithmbased design optimization using reduced models, pp.628-635, 2000. ,
Comparison of methods for developing dynamic reduced models for design optimization, Proceedings of the Congress on Evolutionary Computation (CEC'2002), pp.390-395, 2002. ,
DOI : 10.1007/s00500-003-0331-x
An advanced evolution should not repeat its past errors, International Conference on Machine Learning, pp.400-408, 1996. ,
URL : https://hal.archives-ouvertes.fr/hal-00116421
A genetic algorithm led by induction ,
Using cultural algorithms for constraint handling in genocop, Evolutionary Programming, pp.289-305, 1995. ,
CULTURAL ALGORITHMS: COMPUTATIONAL MODELING OF HOW CULTURES LEARN TO SOLVE PROBLEMS: AN ENGINEERING EXAMPLE, Cybernetics & Systems, vol.36, issue.8, pp.753-771, 2005. ,
DOI : 10.1080/01969720500306147
Hybridization of GRASP Metaheuristic with Data Mining Techniques, Journal of Mathematical Modelling and Algorithms, vol.30, issue.1, pp.23-41, 2006. ,
DOI : 10.1007/s10852-005-9030-1
Hybridization of GRASP Metaheuristic with Data Mining Techniques, Workshop on Hybrid Metaheuristics 16th European Conference on Artificial Intelligence (ECAI), pp.69-78, 2004. ,
DOI : 10.1007/s10852-005-9030-1
Combining an evolutionary algorithm with data mining to solve a single-vehicle routing problem, Neurocomputing, vol.70, issue.1-3, 2006. ,
DOI : 10.1016/j.neucom.2006.07.008
A Hybrid GRASP with Data Mining for the Maximum Diversity Problem, Hybrid Metaheuristic, pp.116-128, 2005. ,
DOI : 10.1007/11546245_11
Controlling evolution by means of machine learning, Evolutionary Programming, pp.57-66, 1996. ,
URL : https://hal.archives-ouvertes.fr/hal-00116419
Controlling crossover through inductive learning, Parallel Problem Solving from Nature ? PPSN III, pp.209-218, 1994. ,
DOI : 10.1007/3-540-58484-6_265
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.56.7922
Toward civilized evolution: Developing inhibitions, Proceeding of the Seventh Int. Conf. on Genetic Algorithms, pp.291-298, 1997. ,
A taxonomy of hybrid metaheuristics, Journal of Heuristics, vol.8, issue.5, pp.541-564, 2002. ,
DOI : 10.1023/A:1016540724870
Hybridising rule induction and multi-objective evolutionary search for optimising water distribution systems, Proceeding of Fourth International Conference on Hybrid Intelligent Systems (HIS'04), pp.434-439, 2004. ,
Clustering Nominal and Numerical Data: A New Distance??Concept??for??a??Hybrid??Genetic??Algorithm, Evolutionary Computation in Combinatorial Optimization ? EvoCOP, pp.220-229, 2004. ,
DOI : 10.1007/978-3-540-24652-7_22
URL : https://hal.archives-ouvertes.fr/inria-00001183
Partially evaluated genetic algorithm based on fuzzy cmeans algorithm, Parallel Problem Solving From Nature (PPSN), pp.440-449, 2004. ,
Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach, IEEE Transactions on Evolutionary Computation, vol.3, issue.4, pp.257-271, 1999. ,
DOI : 10.1109/4235.797969