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Controlling Evolution by Means of Machine Learning.

, , and . Evolutionary Programming, page 57-66. MIT Press, (1996)

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Polar IFS + Individual Genetic Programming = Efficient IFS Inverse Problem Solving, , , and . RR-3849. INRIA, Domaine de Voluceau - Rocquencourt - B.P. 105 78153 Le Chesnay Cedex France, (December 1999)Universal Consistency and Bloat in GP, , , and . Revue d'Intelligence Artificielle, 20 (6): 805--827 (2006)Issue on New Methods in Machine Learning. Theory and Applications.Parametric Coding vs Genetic Programming: A Case Study, , and . Late-Breaking Papers of EuroGP-99, page 13--22. Goteborg, Sweeden, (26-27 May 1999)Using Domain Knowledge in Evolutionary System Identification, and . Evolutionary Methods for Design, Optimization and Control with Applications to Industrial Problems, Athens, (19-21 September 2001)Evolutionary Identification of Macro-Mechanical Models, , , , and . Advances in Genetic Programming 2, chapter 23, MIT Press, Cambridge, MA, USA, (1996)A Dynamic Lattice to Evolve Hierarchically Shared Subroutines., , and . EuroGP, volume 1391 of Lecture Notes in Computer Science, page 220-232. Springer, (1998)Learning to Control Inconsistent Knowledge., and . ECAI, page 479-483. John Wiley and Sons, (1992)An Induction-based Control for Genetic Algorithms (Extended Abstract)., , and . ECML, volume 912 of Lecture Notes in Computer Science, page 351-355. Springer, (1995)A statistical learning theory approach of bloat., , , and . GECCO, page 1783-1784. ACM, (2005)Individual GP: an Alternative Viewpoint for the Resolution of Complex Problems., , , and . GECCO, page 974-981. Morgan Kaufmann, (1999)