|Article title||SEARCH BIOINSPIRED DECISION: THEORY AND APPLICATION TO HANDLE THE PROBLEM-ORIENTED KNOWLEDGE GEOINFORMATICS|
|Authors||S.I. Rodzin, L.S. Rodzina|
|Section||SECTION IV. COMPUTER ENGINEERING AND COMPUTER SCIENCE|
|Month, Year||04, 2015 @en|
|Abstract||Many problems in geoinformatics tasks are reduced to the search for optimal solutions. In order to effectively solve their bioinspired developed algorithms. These species, along with genetic algorithms are algorithms for genetic programming, evolutionary strategies, evolutionary programming, training classifiers, Monte Carlo algorithms, swarm intelligence, memetics, harmonious and others search. One of the main problems faced by their developers is the problem ensure a balance between the rate of convergence of the algorithm and the diversification of the search. This is - the fundamental problem, because of its theoretical and practical importance. For purposeful synthesis bioinspired algorithms developed mechanisms to solve the problem of balance, self-adaptation and adaptation of necessary general theory bioinspired search for optimal solutions. The article discusses the main elements of the theory bioinspired search for optimal solutions. An original memetic algorithms, combining local search, cooperation and competition. Experiments for NP-hard optimization problems have shown that with the help of the developed theory of these problems are solved quickly, reliably and accurately. Bioinspired algorithm - is actively developing field of optimization techniques and decision-making. At the moment, the most promising is the creation of adaptive versions bioinspired search algorithms that take into account the background of the search, as well as task-oriented information on the scope of the search for optimal solutions. The main tool for the construction of new high-performance algorithms bioinspired is hybridization. A feature bioinspired algorithms is the high number of free parameters. From them it may vary depending on the performance of the algorithms. There are only experimental guidelines for choosing the values of these parameters. Further research is needed to adapt and self-adaptation of these parameters.|
|Keywords||Geoinformatics; bioinspired search; memetic algorithm; processing of problem-oriented knowledge.|
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