Theory and algorithms for plan merging
Artificial Intelligence
On the Approximation of Shortest Common Supersequencesand Longest Common Subsequences
SIAM Journal on Computing
Journal of Computer and System Sciences - Special issue on Parameterized computation and complexity
A probabilistic beam search approach to the shortest common supersequence problem
EvoCOP'07 Proceedings of the 7th European conference on Evolutionary computation in combinatorial optimization
Efficient stochastic local search algorithm for solving the shortest common supersequence problem
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Iterative prototype optimisation with evolved improvement steps
EuroGP'06 Proceedings of the 9th European conference on Genetic Programming
Memetic algorithms with partial lamarckism for the shortest common supersequence problem
IWINAC'05 Proceedings of the First international work-conference on the Interplay Between Natural and Artificial Computation conference on Artificial Intelligence and Knowledge Engineering Applications: a bioinspired approach - Volume Part II
A comparison of evolutionary approaches to the shortest common supersequence problem
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
On the Hybridization of Memetic Algorithms With Branch-and-Bound Techniques
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Experimental comparison of six population-based algorithms for continuous black box optimization
Evolutionary Computation
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The Shortest Common Supersequence (SCS) problem is a well-known hard combinatorial optimization problem with applications in many areas. This paper presents two extensions of recently proposed evolutionary-based iterative local search algorithm called POEMS for solving the SCS problem. Both extensions improve scalability of the algorithm. The first one improves the efficiency of the evaluation procedure and the second one further improves optimization capabilities of the algorithm by intensifying the search towards short supersequence already during the process of constructing the valid supersequence. A moderate size benchmark was used for the proof-of-concept experiments while two very large biological benchmarks were used to demonstrate the capability of the proposed approach. The proposed algorithm performs very well on all of the benchmarks. Moreover, it produces significantly better solutions than the baseline Deposition and Reduction algorithm on the two challenging large benchmarks.