In this paper, we take a combinatorial search perspective on generating diverse solutions. We present a generic bi-level optimization framework for finding cost-sensitive diverse solutions. We propose complete methods under this framework, which guarantee finding a set of cost sensitive diverse solutions satisficing the given criteria whenever there exists such a set.
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We identify various aspects that affect the performance of these exhaustive algorithms and propose techniques to improve them. Experimental results show the efficacy of the proposed framework compared to an existing greedy approach. A combinatorial search perspective on diverse solution generation.
N2 - Finding diverse solutions has become important in many combinatorial search domains, including Automated Planning, Path Planning and Constraint Programming. The case studies apply Algorithm Selection techniques to new problem domains and show how to achieve significant performance improvements.
Combinatorial Search: From Algorithms to Systems | Youssef Hamadi | Springer
Lazy learning in constraint solving and the implementation of the alldifferent constraint are the areas in which we improve on the performance of current state of the art systems. The case studies furthermore provide empirical evidence for the effectiveness of using the misclassification penalty as an input to Machine Learning.
After having established the difficulty, we present an effective technique for reducing it. Machine Learning ensembles are a way of reducing the background knowledge and experimentation required from the researcher while increasing the robustness of the system. Ensembles do not only decrease the difficulty, but can also increase the performance of Algorithm Selection systems.
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They are used to much the same ends in Machine Learning itself. We finally tackle one of the great remaining challenges of Algorithm Selection -- which Machine Learning technique to use in practice. Through a large-scale empirical evaluation on diverse data taken from Algorithm Selection applications in the literature, we establish recommendations for Machine Learning algorithms that are likely to perform well in Algorithm Selection for combinatorial search problems.
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The recommendations are based on strong empirical evidence and additional statistical simulations. Login: Password:.
A combinatorial search perspective on diverse solution generation
By: Hamadi, Youssef [author. Contributor s : SpringerLink Online service.
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