Learning the Empirical Hardness of Optimization Problems: The Case of Combinatorial Auctions
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
ACM Computing Surveys (CSUR)
Stochastic Local Search: Foundations & Applications
Stochastic Local Search: Foundations & Applications
INFO-RNA---a fast approach to inverse RNA folding
Bioinformatics
Inverting the Viterbi algorithm: an abstract framework for structure design
Proceedings of the 25th international conference on Machine learning
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Performance prediction and automated tuning of randomized and parametric algorithms
CP'06 Proceedings of the 12th international conference on Principles and Practice of Constraint Programming
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Empirical algorithm study involves tuning various parameter settings in order to achieve an optimal performance. It is also experimentally known that algorithm performance varies across problem instances. In stochastic local search (metaheuristics) paradigm, search efficiency is correlated to the empirical hardness of the underlying combinatorial optimization problem itself. Therefore, investigating these correlations are of crucial importance towards the design of robust algorithmic solutions. To achieve this goal, an accurate prediction of algorithm performance is a prerequisite, since it allows an automatic tuning of parameter settings on a perproblem base. In this work, we investigate using parametric & non-parametric regression models for algorithm performance prediction for the RNA Secondary Structure Design problem (SSD). Empirical results show our non-parametric methods achieve a higher prediction accuracy on biologically existing data, where biological data exhibits a higher degree of local similarity among individual instances. We also found that using a non-parametric regression tree model (CART) provides insight into studying the empirical hardness of solving the SSD problem.