Titlebook: Learning and Intelligent Optimization; 9th International Co Clarisse Dhaenens,Laetitia Jourdan,Marie-Eléonore Conference proceedings 2015

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0302-9743 ynamic optimization, multi-objective, max-clique problems, bayesian optimization and global optimization, data mining and - in a special session - also on dynamic optimization..978-3-319-19083-9978-3-319-19084-6Series ISSN 0302-9743 Series E-ISSN 1611-3349

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Empirical Analysis of Operators for Permutation Based Problems,oach allows us to define a simple generic hyperheuristic that adapt the choice of its operators to the problem at hand and that manages their use in order to ensure a good trade-off between intensification and diversification. Moreover this hyperheuristic can be used on different permutation-based problems.

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Fitness Landscape of the Factoradic Representation on the Permutation Flowshop Scheduling Problem,th the classic permutation representation establishes that local moves on the factoradic representation are less able to lead to the global optima on the PFSP. The study ends by presenting directions for using and improving the factoradic representation.

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Identifying Best Hyperparameters for Deep Architectures Using Random Forests,f deep architectures with respect to hyperparameter variants and to explore underlying interactions of hyperparameters. This is a general method suitable for all types of deep architecture. Our approach is tested by using deep belief network: the error rate reduced from . to . by merely replacing three hyperparameter values.

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Programming by Optimisation Meets Parameterised Algorithmics: A Case Study for Cluster Editing,lly most effective preprocessing algorithm, which yields a 2.-vertex kernel. Notably, this 2.-vertex kernel is analysed empirically for the first time here. Our new algorithm was developed by integrating Programming by Optimisation into the classical algorithm engineering cycle – an approach which we expect to be successful in many other contexts.

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Improving the State of the Art in Inexact TSP Solving Using Per-Instance Algorithm Selection,rovements in practice. Our selectors represent a significant improvement in the state-of-the-art in inexact TSP solving, and hence in the ability to find optimal solutions (without proof of optimality) for challenging TSP instances in practice.

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Conference proceedings 2015hich was held in Lille, France, in January 2015..The 31 contributions presented were carefully reviewed and selected for inclusion in this book. The papers address all fields between machine learning, artificial intelligence, mathematical programming and algorithms for hard optimization problems. Sp