Induction: processes of inference, learning, and discovery
Induction: processes of inference, learning, and discovery
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Virus-evolutionary genetic algorithm for a self-organizing manufacturing system
Computers and Industrial Engineering
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Asynchronous Teams: Cooperation Schemes for Autonomous Agents
Journal of Heuristics
A Roadmap of Agent Research and Development
Autonomous Agents and Multi-Agent Systems
Design and Analysis of Experiments
Design and Analysis of Experiments
International Journal of Bio-Inspired Computation
Computers and Industrial Engineering
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The paper presents a new approach to deal with combinatorial problems. It makes use of a biological analogy inspired by the performance of viruses. The replication mechanism, as well as the hosts' infection processes is used to generate a metaheuristic that allows the obtention of valuable results. The viral system (VS) theoretical context is described and it is applied to a library of medium-to-large-sized cases of the Steiner problem for which the optimal solution is known. The method is compared with the metaheuristics that have provided the best results for the Steiner problem. The VS provides better solutions than genetic algorithms and certain tabu search approaches. For the most sophisticated tabu search approaches (the best metaheuristic approximations to the Steiner problem solution) VS provides solutions of similar quality.