
NonElitist Genetic Algorithm as a Local Search Method
Sufficient conditions are found under which the iterated nonelitist gen...
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Genetic Algorithm with Optimal Recombination for the Asymmetric Travelling Salesman Problem
We propose a new genetic algorithm with optimal recombination for the as...
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Genetic Representations for Evolutionary Minimization of Network Coding Resources
We demonstrate how a genetic algorithm solves the problem of minimizing ...
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Performance Enhancement of Distributed Quasi SteadyState Genetic Algorithm
This paper proposes a new scheme for performance enhancement of distribu...
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Soft Computing approaches on the Bandwidth Problem
The Matrix Bandwidth Minimization Problem (MBMP) seeks for a simultaneou...
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HGmeans: A scalable hybrid genetic algorithm for minimum sumofsquares clustering
Minimum sumofsquares clustering (MSSC) is a widely used clustering mod...
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Reasoning and Facts Explanation in Valuation Based Systems
In the literature, the optimization problem to identify a set of composi...
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Clustering with Penalty for Joint Occurrence of Objects: Computational Aspects
The method of Holý, Sokol and Černý (Applied Soft Computing, 2017, Vol. 60, p. 752762) clusters objects based on their incidence in a large number of given sets. The idea is to minimize the occurrence of multiple objects from the same cluster in the same set. In the current paper, we study computational aspects of the method. First, we prove that the problem of finding the optimal clustering is NPhard. Second, to numerically find a suitable clustering, we propose to use the genetic algorithm augmented by a renumbering procedure, a fast taskspecific local search heuristic and an initial solution based on a simplified model. Third, in a simulation study, we demonstrate that our improvements of the standard genetic algorithm significantly enhance its computational performance.
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