The art of computer programming, volume 3: (2nd ed.) sorting and searching
The art of computer programming, volume 3: (2nd ed.) sorting and searching
Evolution of Non-Deterministic Incremental Algorithms as a New Approach for Search in State Spaces
Proceedings of the 6th International Conference on Genetic Algorithms
More Effective Genetic Search For The Sorting Network Problem
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Statistical analysis of heuristics for evolving sorting networks
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Evolutionary Design of Arbitrarily Large Sorting Networks Using Development
Genetic Programming and Evolvable Machines
Iterative prototype optimisation with evolved improvement steps
EuroGP'06 Proceedings of the 9th European conference on Genetic Programming
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Efficient stochastic local search algorithm for solving the shortest common supersequence problem
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Black-box optimization benchmarking of two variants of the POEMS algorithm on the noiseless testbed
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Comparison of cauchy EDA and pPOEMS algorithms on the BBOB noiseless testbed
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Software project portfolio optimization with advanced multiobjective evolutionary algorithms
Applied Soft Computing
Hyper-Heuristic based on iterated local search driven by evolutionary algorithm
EvoCOP'12 Proceedings of the 12th European conference on Evolutionary Computation in Combinatorial Optimization
Experimental comparison of six population-based algorithms for continuous black box optimization
Evolutionary Computation
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This paper presents an application of a prototype optimization with evolved improvement steps algorithm (POEMS) to the well-known problem of optimal sorting network design. The POEMS is an iterative algorithm that seeks the best variation of the current solution in each iteration. The variations, also called hypermutations, are evolved by means of an evolutionary algorithm. We compared the POEMS to two mutation-based optimizers, namely the (\mu+\lambda)- and (1+\lambda)-evolution strategies. For experimental evaluation 10-input, 12-input, 14-input and 16-input instances of the sorting network problem were used. Results show that the proposed POEMS approach clearly outperforms both compared algorithms. Moreover, POEMS was able to find several perfect networks that are equivalent w.r.t. the number of comparators to the best known solutions for the 10-input, 12-input, 14-input, and 16-input problems. Finally, we propose a modification to the POEMS approach that might further improve its performance.