Article

Article title A COMBINED APPROACH OF ADAPTATION AND SELF-PROCESSING PROBLEM-ORIENTED KNOWLEDGE
Authors E.V. Kuliev, D.Y. Zaporozhets, Vl.Vl. Kureichik
Section SECTION V. NEW INFORMATION TECHNOLOGIES
Month, Year 06, 2015 @en
Index UDC 004.82
DOI
Abstract In the article the problem of a combined approach to the adaptation and self-handling problem-oriented knowledge. The essence of the combined approach is consistent work bionic and genetic algorithms. Bionic algorithm is based on the use patterns of behavior of the colony of bees in the wild. This mechanism allows you to effectively solve the problem of pre-convergence of the algorithm by splitting the search space on a dynamically changing field. The search is performed in parallel in each area, which increases the speed of the algorithm. The solution of the problem demonstrated by the combined methods of exploring the region to find solutions. Presented formulation of the problem of problem-oriented knowledge of the search for optimal solutions. A generalized approach to the construction of the optimality criterion for the solution of the task of handling the problem-oriented knowledge. A modified architecture of a combined approach to solving the problem of handling the problem-oriented knowledge, based on the symbiosis of bioinspired algorithms. This architecture includes the main components of bionic research based on heuristics conduct a swarm of bees in the wild, the unit of evolutionary adaptation necessary to dynamically adjust control parameters and block the external environment, which is the decision maker. Experimental studies in which empirically been confirmed theoretical assessments of the time complexity of the algorithm. It was found that by using a combination of architecture developed, and decreases the chance factor is increased focus algorithm. Series of tests have shown that the time complexity is not beyond the polynomial dependence. At best, the time complexity of algorithms O(nlogn), in the worst case – O(n2).

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Keywords Genetic algorithm; evolutionary algorithm; combined approach; adaptation; neighborhood; population; self-organization; problem-oriented knowledge.
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