. Adriaans, Adriaans (P.) et Zantinge (D.). { Data Mining, 1996.

. Angelano, { A network genetic algorithm for concept learning, Proceedings of the seventh International Conference on Genetic Algorithms ICGA'97, pp.434-441

. Angelano, ). { An experimental evaluation of coevolutive concept learning, Proceedings of the fteenth international conference on machine learning ICML'98, pp.19-27

(. J. Antonisse, { A new interpretation of schema notation that overturns the binary encoding constraint, Proceedings of the third International Conference on Genetic Algorithms ICGA'89, pp.86-91

. Atmar92 and . Atmar, { On the rules and nature of simulated evolutionary programming, Proceedings of the rst Annual Conference on Evolutionary Programming EP'92, pp.17-26

. Augier, { Learning rst order logic rules with a genetic algorithm, KDD-95 Proceedings of The First International Conference on Knowledge Discovery & Data Mining, Montr eal (Canada), pp.21-26

. Augier, { SIAO1, a rst order logic machine learning system using genetic algorithms, ICML'96 13th International Conference on Machine Learning Proceedings of the pre-conference workshop on Evolutionary Algorithms and Machine Learning, pp.9-16

. Augier99 and . Augier, { Changement d' echelle d'un algorithme d'apprentissage automatique In : Actes de la Conf erence d'Apprentissage CAP'99, Plate-forme AFIA'99 { Evolutionary computation : An overview, 235{242. Back et al.96] BB ack (T.) et Schwefel Proceedings of the third IEEE Conference on Evolutionary Computation ICEC'96, pp.20-29, 1999.

. Back96, { Evolutionary Algorithms in Theory and Practice, 1996.

. Badea, { Reenement operators can be (weakly) perfect, Inductive Logic Programming, Ninth International Workshop ILP'99, LNAI 1634, pp.21-32
DOI : 10.1007/3-540-48751-4_4

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.44.2240

. Baker87 and (. J. Baker, { Reducing bias and ineeciency in the selection algorithm, Genetic Algorithms and their Applications : Proceedings of the second International Conference ICGA'87, pp.14-21

. Bauer, { An empirical comparison of voting classiication algorithms : Bagging, boosting and variants, Machine Learning, pp.105-139, 1999.

. Baxter77 and . Baxter, { The NP-completeness of subsumption

. Belding95 and . Belding, { The distributed genetic algorithm revisited, Proceedings of the sixth International Conference on Genetic Algorithms ICGA'95, pp.114-121

. Blockeel, Top-down induction of first-order logical decision trees, Artificial Intelligence, vol.101, issue.1-2, pp.285-297, 1998.
DOI : 10.1016/S0004-3702(98)00034-4

. Blockeel, { Scaling up inductive logic programming by learning from interpretations, Data Mining and Knowledge Discovery, vol.3, issue.1, pp.59-93, 1999.
DOI : 10.1023/A:1009867806624

. Breimann96 and . Breimann, Bagging predictors, Machine Learning, pp.123-140, 1996.
DOI : 10.1007/BF00058655

. Brezellec99 and . Br-ezellec, { Sondage et apprentissage In : Actes de la Conf erence d'Apprentissage CAP'99, Plate-forme AFIA'99, pp.63-68, 1999.

. Brunet93 and . Brunet, { Le probl eme de la r esistance aux valeurs inconnues dans l'induction : une for^ et de branches In : Actes des huiti emes journ ees francophones sur l'apprentissage JFA'93, Saint Rapha el

. Brunet94 and . Brunet, { Pr esentation de StatLog (projet ESPRIT 5170)

. Cochran77 and . Cochran, { Sampling Techniques { Selection in massively parallel genetic algorithms, Collins et al.91] Collins (R.J.) et Jeeerson Proceedings of the fourth International Conference on Genetic Algorithms ICGA'91, pp.249-256, 1977.

. Cramer85 and . Cramer, { A representation for the adaptive generation of simple sequential programs, Proceedings of the rst International Conference on Genetic Algorithms and their applications, pp.183-187

. Davis89 and . Davis, { Adapting operator probabilities in genetic algorithms, Proceedings of the third International Conference on Genetic Algorithms ICGA'89, pp.61-69

. De-jong, { Learning concept classiication rules using genetic algorithms, Proceedings of the Twelfth International Joint Conference on Artiicial Intelligence IJCAI'91, pp.651-656

. De-jong, ). { Using genetic algorithms for concept learning, Machine Learning, pp.161-188, 1993.

. De-jong, { On decentralizing selection algorithms, Proceedings of the sixth International Conference on Genetic Algorithms ICGA'95, pp.17-23

D. Jong and (. K. , { Machine Learning -An Artiicial Intelligence Approach , Volume III, chap. Genetic-Algorithm-Based Learning, pp.611-638, 1990.

D. Jong and (. K. , { Genetic algoritms are NOT function optimizers, Foundations of Genetic Algorithms 2 FOGA'92, pp.5-18

D. Raedt, Logical settings for concept-learning, Artificial Intelligence, vol.95, issue.1, pp.187-201, 1997.
DOI : 10.1016/S0004-3702(97)00041-6

. Debnath, Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds. Correlation with molecular orbital energies and hydrophobicity, Journal of Medicinal Chemistry, vol.34, issue.2, pp.786-797, 1991.
DOI : 10.1021/jm00106a046

. Diday, { Induction symbolique-num erique a partir de donn ees { Machine learning research -four current directions, pp.97-136, 1997.

. Doorly95 and . Doorly, { Genetic Algorithms in Engeneering and Computer Science, chap. Parallel Genetic Algorithms for Optimization in CFD, 1995.

J. R. Ehrenburg96-]-ehrenburg, D. E. Koza, D. B. Goldberg, R. L. Fogel, and . Riolo, { Improved directed acyclic graph evaluation and the combine operator in genetic programming { The use of multiple measurements in taxonomic problems, Proceedings of the rst annual conference GP'96 179{188. Fleury96] Fleury (L.). { Mesure de la Qualit e d'une R egle, p.210, 1936.

(. M. Flynn66-]-flynn, Very high-speed computing systems, Proceedings of the IEEE, vol.54, issue.12, 1966.
DOI : 10.1109/PROC.1966.5273

. Frawley, { Knowledge Discovery in Databases, chap. Knowledge Discovery in Databases : An Overview, 1991.

. Freund, { Experiments with a new boosting algorithm, Proceedings of the thirteenth International Conference on Machine Learning ICML'96, pp.148-156

. Geist, PVM-Parallel Virtual Machine: AUsers' Guide and Tutorial for Networked Parallel Computing, Computers in Physics, vol.9, issue.6, 1994.
DOI : 10.1063/1.4823450

. Giordana, 92] Giordana (A.) et Sale (C.). { Genetic algorithms for learning relations, ICML'92, pp.169-178

. Glover, A user's guide to tabu search, Taillard (E.) et de Werra, pp.3-28, 1993.
DOI : 10.1007/BF02078647

. Goldberg, { Genetic algorithms with sharing for multimodal function optimization, Genetic Algorithms and their Applications : Proceedings of the second International Conference ICGA'87, pp.41-49

. Goldberg87 and (. D. Goldberg, { Simple genetic algorithms and the minimal, deceptive problem, In : Genetig Algorithms and Simulated Annealing, pp.74-88

. Goldberg89 and (. D. Goldberg, { Genetic Algorithms in Search, Optimization and Machine Learning, 1989.

. Gottlob, Removing redundancy from a clause, Artificial Intelligence, vol.61, issue.2, pp.263-289, 1993.
DOI : 10.1016/0004-3702(93)90069-N

. Gottlob87 and . Gottlob, Subsumption and implication, Information Processing Letters, vol.24, issue.2, pp.109-111, 1987.
DOI : 10.1016/0020-0190(87)90103-7

(. J. Grefenstette81-]-grefenstette, { Parallel Adaptive Algorithms for Function Optimization, 1981.

. Grefenstette95 and . Grefenstette, { Virtual Genetic Algorithms : First Results, 1995.

(. J. Gustafson88-]-gustafson, Reevaluating Amdahl's law, Communications of the ACM, vol.31, issue.5, pp.532-533, 1988.
DOI : 10.1145/42411.42415

. Haussler88 and . Haussler, Quantifying inductive bias: AI learning algorithms and Valiant's learning framework, Artificial Intelligence, vol.36, issue.2, pp.177-221, 1988.
DOI : 10.1016/0004-3702(88)90002-1

. Haynes96 and . Haynes, { Duplication of coding segments in genetic programming, Proceedings of the thirteenth national conference on artiicial intelligence AAAI'96, pp.344-349

. Hekanaho97 and . Hekanaho, { GA-based rule enhancement in concept-learning

. Hekanaho98 and . Hekanaho, DOGMA: A GA-based relational learner, Inductive Logic Programming, Eighth International Conference ILP'98
DOI : 10.1007/BFb0027324

(. J. Holland75-]-holland, { Adaptation in Natural and Artiicial Systems, 1975.

. Holland86 and . Holland, { Machine Learning -An Artiicial Intelligence Approach, Escaping Brittleness : the Possibilities BIBLIOGRAPHIE 223

. Kaufman, { Learning in an inconsistent world : rule selection in AQ18, Machine Learning and Inference Laboratory, pp.22030-4444, 1999.

. Kietz, { An eecient subsumption algorithm for inductive logic programming, Proceedings of the Eleventh International Conference on Machine Learning ML'94, pp.130-138, 1994.

. Kim, A new subsumption method in the connection graph proof procedure, Theoretical Computer Science, vol.103, issue.2, pp.283-309, 1992.
DOI : 10.1016/0304-3975(92)90016-9

. Kodratoo94 and . Kodratoo, { Guest Editor's Introduction ( The Comprehensibility Manifesto ), AI Communications, vol.7, issue.2, pp.83-85, 1994.

. Kodratoo95 and . Kodratoo, { Algorithmic Learning Theory, chap. Technical and Scientiic Issues of KDD (or : Is KDD a Science ?, Lecture Notes in Artiicial Intelligence, vol.997, 1995.

. Kodratoo98 and . Kodratoo, { IBM Data-Mining Course (Li ege), 1998.

. Kodratoo00 and . Kodratoo, { chap. Applications de l'apprentissage automatique et de la fouille de donn ees, 2000.

. Kohavi95 and . Kohavi, { A study of cross-validation and bootstrap for accuracy estimation and model selection, Proceedings of the Fourteenth International Joint Conference on Artiicial Intelligence (IJCAI'95), pp.1137-1143

. Koza89 and . Koza, { Hierarchical genetic algorithms operating on populations of computer programs, Proceedings of the Eleventh International Joint Conference on Artiicial Intelligence IJCAI'89, pp.768-774

. Koza92 and . Koza, { Genetic Programming, Koza94] Koza (J.R.). { Genetic Programming 2, 1992.

. Lavrac, Inductive logic programming for relational knowledge discovery, New Generation Computing, vol.5, issue.20, pp.3-23, 1999.
DOI : 10.1007/BF03037580

. Lincoo81 and . Lincoo, { The Audubon Society Field Guide to North American Mushrooms, 1981.

L. Bello, { A Coevolutionary Distributed Approach to Learning Classiication Programs (preliminary version), 1998.

. Maloberti00 and . Maloberti, { Apports de la Satisfaction de Contraintes a la Programmation Logique Inductive : un nouvel algorithme de subsomption . { Technical report, LRI, 5 septembre, 2000.

. Manderick, { Fine-grained parallel genetic algorithms, Proceedings of the third International Conference on Genetic Algorithms ICGA'89, pp.428-433

(. C. Merz99-]-merz, { Using correspondance analysis to combine classiiers, Machine Learning, pp.33-58, 1999.

. Michalski80 and . Michalski, Pattern Recognition as Rule-Guided Inductive Inference, IEEE transactions on Pattern Analysis and Machine Intelligence , PAMI-2, pp.349-361, 1980.
DOI : 10.1109/TPAMI.1980.4767034

(. R. Michalski84-]-michalski, { Machine Learning : An AI Approach A theory and methodology of inductive learning, 1984.
DOI : 10.1007/978-3-662-12405-5

. Mitchell, { The royal road for genetic algorithms : Fitness landscapes and GA performance, Toward a Practice of Autonomous Systems : Proceedings of the First European Conference on Artiicial Life par Varela (F.J.) et Bourgine

. Mitchell77 and . Mitchell, { Version spaces : A candidate elimination approach to rule learning, Fifth International Joint Conference on Artiicial Intelligence IJCAI'77, pp.305-310

. Mitchell80 and . Mitchell, { The need for biases in learning generalizations, 1980.

. Mitchell97 and . Mitchell, { Machine Learning, 1997.

(. D. Montana95-]-montana, Strongly Typed Genetic Programming, Evolutionary Computation, vol.3, issue.2, pp.199-230, 1995.
DOI : 10.1115/1.3662552

. Muggleton, { Machine invention of rst order predicates by inverting resolution, Proceedings of the Fifth international Machine Learning Workshop, pp.339-352

. Muggleton, 90] Muggleton (S.) et Feng (C.). { EEcient induction of logic programs, Proceedings of the rst conference on Algorithmic Learning Theory ALT'90

. Muggleton, { Machine invention of rst-order predicates by inverting resolution, Inductive Logic Programming par Muggleton (S.), pp. 261{280. { Associated Press, 1992.

. Muggleton, Inductive Logic Programming: Theory and methods, The Journal of Logic Programming, vol.19, issue.20, pp.629-679, 1994.
DOI : 10.1016/0743-1066(94)90035-3

URL : http://doi.org/10.1016/0743-1066(94)90035-3

. Muggleton90 and . Muggleton, Inductive logic programming, New Generation Computing, vol.7, issue.1, pp.295-318, 1990.
DOI : 10.1007/BF03037089

. Muggleton92 and . Muggleton, { Inductive Logic Programming. { Academic Press, 1992. The A.P.I.C. Series

. Muggleton95 and . Muggleton, Inverse entailment and progol, New Generation Computing, vol.12, issue.1, pp.245-286, 1995.
DOI : 10.1007/BF03037227

. Munteanu96 and . Munteanu, { Extraction de Connaissances dans les Bases de Donn ees Parole : Apport de l'Apprentissage Symbolique, NC et al.97] Nienhuys-Cheng (S.-H.) et de Wolf (R.). { Foundations of Inductive Logic Programming. { Springer, LNAI 1228, 1996.

. Nedellec, { First Order Logic Concept Learning by Means of a Distributed Genetic Algorithm, Bergadano (F.) et Tausend (B.). { Advances in Inductive Logic Programming, chap. Declarative Bias in Inductive Logic Programming, 1996.

. Periaux, { Genetic Algorithms in Engeneering and Computer Science, chap. Robust Genetic Algorithms for Optimization Problems in Aerodynamic Design, pp.371-396, 1995.

. Pettey, { A theoretical investigation of a parallel genetic algorithm, Proceedings of the third International Conference on Genetic Algorithms ICGA'89, pp.398-405

(. G. Plotkin70-]-plotkin, { Machine Intelligence, pp.153-163, 1970.

. Provost, { Scaling up inductive machine learning with massive parallelism, Machine Learning, pp.33-46, 1996.

. Provost, { Analysis and visualization of classiier performance : Comparison under imprecise class and cost distributions, Proceedings of the Third International Conference on Knowledge Discovery and Data Mining KDD'97

. Quinlan, FOIL: A midterm report, European Conference on Machine Learning, pp.3-20
DOI : 10.1007/3-540-56602-3_124

. Quinlan, Induction of logic programs: FOIL and related systems, New Generation Computing, vol.5, issue.1, pp.3-4, 1995.
DOI : 10.1007/BF03037228

. Quinlan86 and . Quinlan, Induction of decision trees, Machine Learning, pp.81-106, 1986.
DOI : 10.1007/BF00116251

. Quinlan87 and . Quinlan, { Generating production rules from decision trees, Proceedings of the Tenth International Joint Conference on Artiicial Intelligence IJCAI'87, pp.304-307

. Quinlan93 and . Quinlan, { C4.5 -Programs For Machine Learning, 1993.

. Rakotomalala97 and . Rakotomalala, { Graphes d'Induction, 1997.

. Ravise, Ravis e (C.) et Sebag (M.). { An advanced evolution should not repeat its past errors, Proceedings of the thirteenth International BIBLIOGRAPHIE 227

. Reiser, Evolution of logic programs: part-of-speech tagging, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406), pp.1338-1345
DOI : 10.1109/CEC.1999.782604

. Richards, { Learning relations by pathhnding, Proceedings of the tenth national conference on artiicial intelligence AAAI'92, pp.51-55

. Sarma, { An analysis of local selection algorithms in a spatially structured evolutionary algorithm, Proceedings of the seventh International Conference on Genetic Algorithms ICGA'97, pp.181-186

. Schaaer, { Spurious correlations and premature convergence in genetic algorithms, Foundations of Genetic Algorithms 1 FOGA'90, pp.102-112

. Schaaer94 and . Schaaer, { A conservation law for generalization performance, Proceedings of the Eleventh International Conference on Machine Learning ML'94, pp.259-265, 1994.

. Scheeer, { EEcient algorithms for -subsumption, Herbrich (R.) et Wysotzki Inductive Logic Programming, 6 th International Workshop, LNAI1314, pp.212-228

. Schoenauer, { Evolutionary computation, et Michalewicz, pp.307-338, 1997.

. Sebag, { Tractable induction and classiication in rst order logic via stochastic matching, Fifteenth International Joint Conference on Artiicial Intelligence IJCAI'97, pp.888-893

. Sebag, Schoenauer (M.) et Ravis e (C.). { Inductive learning of mutation step-size in evolutionary parameter optimization, th Annual Conference on Evolutionary Programming EP'97 LNCS 1213, pp.247-261

. Sebag94 and . Sebag, Using constraints to building version spaces, European Conference on Machine Learning, pp.257-271, 1994.
DOI : 10.1007/3-540-57868-4_63

. Sebag96 and . Sebag, { Delaying the choice of bias : A disjunctive version space approach, Proceedings of the thirteenth International Conference on Machine Learning ICML'96, pp.444-452

. Sebag98 and . Sebag, A stochastic simple similarity, Eighth International Conference ILP'98, pp.1446-95
DOI : 10.1007/BFb0027313

. Silverstein, { Relational clich es : Constraining constructive induction during relational learning, Proceedings of the Eight International Workshop on Machine Learning ML'91, pp.203-207
DOI : 10.1016/b978-1-55860-200-7.50044-1

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.74.9835

. Smith83 and . Smith, { Flexible learning of problem solving heuristics through adaptive search, Proceedings of the Eighth International Joint Conference on Artiicial Intelligence IJCAI'83, pp.422-425

. Sommer95 and . Sommer, { An approach to quantifying the quality of induced theories, Proceedings of IJCAI'95 Workshop on Machine Learning and Comprehensibility

. Spears, An overview of evolutionary computation, Sixth European Conference on Machine Learning ECML'93 LNAI 667, pp.443-459
DOI : 10.1007/3-540-56602-3_163

. Spears91 and . Spears, { Adapting crossover in a genetic algorithm, Proceedings of the fourth International Conference on Genetic Algorithms ICGA'91, pp.166-173

. Spiessens, 91] Spiessens (P.) et Manderick (B.). { A massively parallel genetic algorithm, Proceedings of the fourth International Conference BIBLIOGRAPHIE 229

. Srinivasan, { Mutagenesis : ILP experiments in a non-determinate biological domain { Implication of clauses is undecidable, Proceedings of the Fourth International Workshop on Inductive Logic Programming ILP'94 GMD-Studien Nr. 237, pp.287-296, 1988.

. Syswerda89 and . Syswerda, { Uniform crossover in genetic algorithms). { Searching the subsumption lattice by a genetic algorithm, Proceedings of the third International Conference on Genetic Algorithms ICGA'89 Inductive Logic Programming , Tenth International Conference ILP'2000 LNAI 1866, pp.243-252

. Tsang93 and . Tsang, { Foundations of Constraint Satisfaction, 1993.

(. P. Utgoo86-]-utgoo, { Machine Learning -An Artiicial Intelligence Approach , Volume II, chap. Shift of bias for inductive concept learning, Vapnik98] Vapnik (V.). { Advances in Kernel Methods -Support Vector Learning , chap. Three Remarks on the Support Vector Method of Function Estimation, pp.25-41, 1986.

. Venturini93 and . Venturini, ANALYZING FRENCH JUSTICE WITH A GENETIC-BASED INDUCTIVE ALGORITHM, Sixth European Conference on Machine Learning ECML'93 LNAI 667, pp.565-577, 1994.
DOI : 10.1007/BF00113894

. Venturini94b and . Venturini, { Apprentissage Adaptatif et Apprentissage Supervis e par Algorithme G en etique, p.3050, 1994.

. Venturini95 and . Venturini, Towards a genetic theory of easy and hard functions, Artiicial Evolution -European Conference AE'95
DOI : 10.1007/3-540-61108-8_30

. Venturini96 and . Venturini, { Algorithmes g en etiques et apprentissage, pp.345-387, 1996.

. Vose92 and . Vose, { Modeling simple genetic algorithm, Foundations of Genetic Algorithms 2 FOGA'92, pp.63-73

. Whitley91 and . Whitley, Fundamental Principles of Deception in Genetic Search, Foundations of Genetic Algorithms 1 FOGA'90, pp.221-241
DOI : 10.1016/B978-0-08-050684-5.50017-3

. Wilson85 and . Wilson, { Knowledge growth in an artiicial animal, Proceedings of the rst International Conference on Genetic Algorithms and their applications ICGA'85, pp.16-23

(. S. Wilson87-]-wilson, { Classiier systems and the animat problem, Machine Learning, pp.199-228, 1987.

. Wilson91 and . Wilson, { GA-easy does not imply steepest-ascent optimizable, Proceedings of the fourth International Conference on Genetic Algorithms ICGA'91, pp.85-89

. Wilson98 and . Wilson, { Generalization in the XCS classiier system, Proceedings of the Third Annual Conference, 1998.

. Wilson99 and . Wilson, { State of XCS Classiier System Research, Prediction Dynamics, 1999.

. Winston75 and . Winston, { The Psychology of Computer Vision, chap. Learning Structural Descriptions from Examples, 157{209. { Mc- Graw-Hill, 1975.

. Wolpert, No free lunch theorems for optimization, IEEE Transactions on Evolutionary Computation, vol.1, issue.1, 1996.
DOI : 10.1109/4235.585893

. Wolpert, { No Free Lunch Theorems for Search, 1996.

. Wong, Inducing logic programs with genetic algorithms: the Genetic Logic Programming System, IEEE Expert, vol.10, issue.5, pp.68-76, 1995.
DOI : 10.1109/64.464935

. Zheng, 00] Zheng (Z.) et Webb (G.I.). { Lazy learning of bayesian rules, Machine Learning, pp.53-84, 2000.

. Zighed, ). { Graphes d'Induction - Apprentissage en Data Mining, 2000.

. Zitzler99 and . Zitzler, { Evolutionary Algorithms for Multiobjective Optimization : Methods and Applications

. Zucker, { Representation changes for eecient learning in structural domains, Proceedings of the thirteenth International Conference on Machine Learning ICML'96, pp.543-551

. Zucker, Learning structurally indeterminate clauses, Inductive Logic Programming, pp.235-244
DOI : 10.1007/BFb0027327

.. Op-erateurs-de-reproduction-de-l-'agc, 40 2.4 D eroulement d'un cycle de l'algorithme g en etique canonique, p.41

.. Fen^-etre-de-description-d-'un-type-num-erique, 215 236 LISTE DES FIGURES Liste des tableaux 1.1 Co^ ut de d eveloppement et de maintenance de dii erents syst emes experts, suivant qu'ils mettent en uvre ou non des techniques d'apprentissage automatique (source : Muggleton92])

.. Strat-egies-de-r-esolution, 8 1.3.1 Sp eciicit e du probl eme, p.13

A. Biais, 24 1.4.1 D eenition et caract erisation des biais, p.26

.. Apprentissage-en-logique-du-premier-ordre, 29 1.5.1 Le langage de la logique relationnelle, p.32

.. Les-algorithmes-d-'evolution, 35 2.1.1 Introduction, p.39

. Algorithmes-d-'evolution-et-apprentissage-automatique........, 54 2.2.1 Approche de Michigan -Les syst emes de classeurs, p.55

.. Implantation-du-test-de-subsomption, 83 3.2.1 Raisons motivant l'optimisation 83 3.2.2 Architecture g en erale

E. Et-mutagenesis, .. Et-adult, and .. , 151 4.6.1 R esultats sur Iris et Mushrooms, p.154