Distributed genetic algorithms
Proceedings of the third international conference on Genetic algorithms
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Almost tight bounds for &egr;-nets
Discrete & Computational Geometry
A threshold of ln n for approximating set cover (preliminary version)
STOC '96 Proceedings of the twenty-eighth annual ACM symposium on Theory of computing
A survey of approximately optimal solutions to some covering and packing problems
ACM Computing Surveys (CSUR)
Approximation of k-set cover by semi-local optimization
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
A tight analysis of the greedy algorithm for set cover
Journal of Algorithms
Parallel island-based genetic algorithm for radio network design
Journal of Parallel and Distributed Computing - Special issue on parallel evolutionary computing
Improved performance of the greedy algorithm for partial cover
Information Processing Letters
Automated antenna positioning algorithms for wireless fixed-access networks
Journal of Heuristics
Optimisation of WCDMA radio networks with consideration of link-level performance factors
International Journal of Mobile Network Design and Innovation
Radio Network Design Using Population-Based Incremental Learning and Grid Computing with BOINC
Proceedings of the 2007 EvoWorkshops 2007 on EvoCoMnet, EvoFIN, EvoIASP,EvoINTERACTION, EvoMUSART, EvoSTOC and EvoTransLog: Applications of Evolutionary Computing
Evaluation of Different Metaheuristics Solving the RND Problem
Proceedings of the 2007 EvoWorkshops 2007 on EvoCoMnet, EvoFIN, EvoIASP,EvoINTERACTION, EvoMUSART, EvoSTOC and EvoTransLog: Applications of Evolutionary Computing
Benchmarking a wide spectrum of metaheuristic techniques for the radio network design problem
IEEE Transactions on Evolutionary Computation
Using omnidirectional BTS and different evolutionary approaches to solve the RND Problem
EUROCAST'07 Proceedings of the 11th international conference on Computer aided systems theory
Genetic Programming and Evolvable Machines
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This paper uses a realistic problem taken from the telecommunication world as the basis for comparing different combinatorial optimization algorithms. The problem recalls the minimum hitting set problem, and is solved with greedy-like, Darwinism and genetic algorithms. These three paradigms are described and analyzed with emphasis on the Darwinism approach, which is based on the computation of &egr;-nets.