American Journal of Mathematical and Management Sciences - Modern digital simulation methodology, III
A systematic procedure for setting parameters in simulated annealing algorithms
Computers and Operations Research
Using Experimental Design to Find Effective Parameter Settings for Heuristics
Journal of Heuristics
Hybrid Genetic Algorithm for DNA Sequencing with Errors
Journal of Heuristics
MFCS '94 Proceedings of the 19th International Symposium on Mathematical Foundations of Computer Science 1994
Complexity of DNA sequencing by hybridization
Theoretical Computer Science
IBM Journal of Research and Development
DNA Sequencing--Tabu and Scatter Search Combined
INFORMS Journal on Computing
DNA Sequencing by Hybridization via Genetic Search
Operations Research
Fine-Tuning of Algorithms Using Fractional Experimental Designs and Local Search
Operations Research
Engineering Applications of Artificial Intelligence
Computers and Operations Research
Algorithm: Dealing with repetitions in sequencing by hybridization
Computational Biology and Chemistry
Matheuristics: Hybridizing Metaheuristics and Mathematical Programming
Matheuristics: Hybridizing Metaheuristics and Mathematical Programming
Hybrid metaheuristics in combinatorial optimization: A survey
Applied Soft Computing
Fine-Tuning algorithm parameters using the design of experiments approach
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
A hybrid algorithm for the DNA sequencing problem
Discrete Applied Mathematics
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One of the key issues in designing an algorithm in general, and a metaheuristic in particular, concerns the fine tuning of one or more algorithmic parameters. In this paper, we present a simple mechanism aimed at automatically fine tuning a parameter of a novel hybrid algorithm. We design an algorithm that uses mathematical programming techniques in a metaheuristic fashion and we exploit ideas from the corridor method to drive the use of a standard MIP solver over different portions of the solution space. The size and the boundaries of such portions of the solution space are determined by the width of the corridor built around an incumbent solution. In turn, the corridor width is automatically fine tuned by the proposed mechanism, taking into account the evolution of the search process. The proposed algorithm is then tested on a well known problem from computational biology and results on a set of benchmark instances are provided.