The AETG System: An Approach to Testing Based on Combinatorial Design
IEEE Transactions on Software Engineering
Computers and Operations Research
In-Parameter-Order: A Test Generation Strategy for Pairwise Testing
HASE '98 The 3rd IEEE International Symposium on High-Assurance Systems Engineering
Variable neighborhood decomposition search for the edge weighted k-cardinality tree problem
Computers and Operations Research
Designs, Codes and Cryptography
IPOG: A General Strategy for T-Way Software Testing
ECBS '07 Proceedings of the 14th Annual IEEE International Conference and Workshops on the Engineering of Computer-Based Systems
An effective two-stage simulated annealing algorithm for the minimum linear arrangement problem
Computers and Operations Research
Upper bounds for covering arrays by tabu search
Discrete Applied Mathematics
Memetic algorithms for the MinLA problem
EA'05 Proceedings of the 7th international conference on Artificial Evolution
Verification of general and cyclic covering arrays using grid computing
Globe'10 Proceedings of the Third international conference on Data management in grid and peer-to-peer systems
A survey of methods for constructing covering arrays
Programming and Computing Software
New bounds for binary covering arrays using simulated annealing
Information Sciences: an International Journal
Optimal shortening of covering arrays
MICAI'11 Proceedings of the 10th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
An improved memetic algorithm for the antibandwidth problem
EA'11 Proceedings of the 10th international conference on Artificial Evolution
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This paper presents a new Memetic Algorithm (MA) designed to compute near-optimal solutions for the covering array construction problem. It incorporates several distinguished features including an efficient heuristic to generate a good quality initial population, and a local search operator based on a fine tuned Simulated Annealing (SA) algorithm employing a carefully designed compound neighborhood. Its performance is investigated through extensive experimentation over well known benchmarks and compared with other state-of-the-art algorithms, showing improvements on some previous best-known results.