Dissertation Corner Handling Constrained Optimization Problems and Using Constructive Induction to Improve Representation Spaces in Learnable Evolution Model Doctoral Thesis by Janusz Wojtusiak The learnable evolution model (LEM) is an evolutionary optimization method which uses machine learning to guide the evolution process (Michalski, 2000). At each step of evolution a machine learning program is applied to induce hypotheses why some candidate solutions perform better and others perform worse. These hypotheses are then instantiated in order to produce new candidate solutions. This dissertation investigates two closely related problems in the learnable evolution model: the automatic improvement of representation spaces using constructive induction, and the handling of constraints in optimization problems. The former includes an investigation of different aspects of representation space transformations in the context of optimization problems, the development of algorithms that perform these transformations, and algorithms for creating new candidate solutions (via instantiation) in the improved representation spaces. Handling speci c types of constraints is closely related to the instantiation task in the modi ed representation spaces; therefore, the same methods can be used for solving both problems. Moreover, transformations of representation spaces may help in handling constraints of other types, that is, constraints that cannot be
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Dissertation corner: Handling constrained optimization problems and using constructive induction to improve representation spaces in learnable evolution model
Wojtusiak, Janusz
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, Volume 2 (3)
Association for Computing Machinery – Sep 1, 2007
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