On some variants of the bandwidth minimization problem
SIAM Journal on Computing
A study of permutation crossover operators on the traveling salesman problem
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
The Obnoxious Center Problem on a Tree
SIAM Journal on Discrete Mathematics
AllelesLociand the Traveling Salesman Problem
Proceedings of the 1st International Conference on Genetic Algorithms
A Survey on Obnoxious Facility Location Problems
A Survey on Obnoxious Facility Location Problems
Fine-Tuning of Algorithms Using Fractional Experimental Designs and Local Search
Operations Research
Applying adaptive algorithms to epistatic domains
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 1
Memetic algorithms for constructing binary covering arrays of strength three
EA'09 Proceedings of the 9th international conference on Artificial evolution
Memetic algorithm for the antibandwidth maximization problem
Journal of Heuristics
MiTS: a new approach of tabu search for constructing mixed covering arrays
MICAI'10 Proceedings of the 9th Mexican international conference on Artificial intelligence conference on Advances in soft computing: Part II
Handbook of Memetic Algorithms
Handbook of Memetic Algorithms
Fine-Tuning algorithm parameters using the design of experiments approach
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
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This paper presents an Improved Memetic Algorithm (IMA) designed to compute near-optimal solutions for the antibandwidth problem. It incorporates two distinguishing features: an efficient heuristic to generate a good quality initial population and a local search operator based on a Stochastic Hill Climbing algorithm. The most suitable combination of parameter values for IMA is determined by employing a tunning methodology based on Combinatorial Interaction Testing. The performance of the fine-tunned IMA algorithm is investigated through extensive experimentation over well known benchmarks and compared with an existing state-of-the-art Memetic Algorithm, showing that IMA consistently improves the previous best-known results.