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Article title BIOMEMETICS − INTEGRATION OF MACHINE LEARNING ALGORITHMS AND EVOLUTIONARY COMPUTATION
Authors S.I. Rodzin
Section SECTION I. EVOLUTIONARY MODELLING, GENETIC AND BIONIC ALGORITHMS
Month, Year 07, 2014 @en
Index UDC 004.81
DOI
Abstract Biomemetics – actively developing area of evolutionary computation and methods of optimization. Mem – a unit of cultural information that can be transmitted from person to person by imitation or learning. In biomemetics concept of evolutionary theory (population genetics) are transferred to human culture. Defined metrics and submetrics for evaluating of memes: propagation, persistence, entropy, impact. With the help of the analytic hierarchy the estimate of each priority metrically properties. The algorithm biometetics, built his syntactic model. The main components of the algorithm are biomemetics local search, cooperation, competition, end criterion search. Integration of evolutionary algorithm with machine learning is that the evolutionary algorithm includes a procedure of individual training individuals with information about the structure of the space of possible solutions. Biomemetics algorithm is illustrated by the scheduling problem: given a set of requirements and a set of objects on which requirements should be maintained in accordance with certain procedures, it is necessary to find a schedule that satisfies the data constraints. The results of experiments. Quality of the solutions found better compared to known genetic algorithms.

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Keywords Evolutionary computation; biomemetics; task schedule.
References 1. Darvin Ch. Proiskhozhdenie vidov putem estestvennogo otbora ili sokhranenie blagopriyatnykh ras v borbe za zhizn [The origin of species by means of natural selection or the preservation of favorable races in the struggle for life]. Saint-Petersburg: Nauka, 1991, 540 p.
2. Dokinz R. Egoistichnyy gen [The selfish gene]. Moscow: Mir, 1993. Available
at:http://modernlib.ru/books/dokinz_richard/.
3. Rodzin S.I. Metrika i algoritmy memetiki [Metrics and algorithms of memetics], Vestnik RGUPS [Herald Rostov State University Railway], 2013, No. 4 (52), pp. 59-67.
4. Saati T. Prinyatie resheniy. Metod analiza ierarkhiy [Making decisions. The method of analysis of hierarchies]. Moscow: Radio i svyaz, 1993, 278 p.
5. Moscato P. Memetic algorithms Handbook of Applied Optimization. 2002, pp. 157-167.
6. Kureychik V.V., Kureychik V.M., Rodzin S.I. Teoriya evolyutsionnykh vychisleniy [The theory of evolutionary computation]. Moscow: Fizmatlit, 2012, 260 p.
7. Reynolds R.G. An Introduction to cultural algorithms, Proc. of the third annual conf. on Evolutionary Programming, 1994, pp. 131-139.
8. Merz P. Fitness landscapes, memetic algorithms and greedy operators for graph bipartitioning, Evolutionary Computation, 2000, No. 8, pp. 61-91.
9. Costa D. Embedding of a sequential procedure within an evolutionary algorithm for coloring problems in graphs, Journal of heuristics, 1995, No. 1, pp. 105-112.
10. Beasley J. A genetic algorithm for the set covering problem, European journal of operational research, 1996, No 2 (94), pp. 393-404.
11. Beasley J. A genetic algorithm for the multidimensional knapsack problem, Journal of heuristics, 1998, No. 4, pp. 63-86.
12. Merz P. Greedy and local search heuristics for the unconstrained binary quadratic programming problem, Journal of heuristics, 2002, No. 2, pp. 197-213.
13. Krasnogor N., Smith J. A memetic algorithm with selfadaptive local search: TSP as a case study, Proc. of the Genetic and Evolutionary Computation Conference (GECCO-2000). Las Vegas, USA: Morgan Kaufmann, pp. 987-994.
14. Kureichik V.V., Kravchenko Y.A. Bioinspired algorithm applied to solve the travelling sales-man problem, World Applied Sciences Journal, 2013, Vol. 22, No. 12, pp. 1789-1797.
15. Topchy A., Lebedko ., Miagkikh V. Fast learning in multilayered networks by means of hybrid evolutionary and gradient algorithms, Proc. of int. conf. on evolutionary computation and its applications, 1996, pp. 390-398.
16. Areibi S. An integrated genetic algorithm with dynamic hill climbing for VLSI circuit partitioning, Proc. of int. conf. on DM&EA. 2000, pp. 97-102.
17. Kureichik V.M., Rodzin S.I. Evolutionary algorithms: genetic programming, Journal of Computer and Systems Sciences International, 2002, Vol. 41, No. 1, pp. 123-132.
18. Bartenev A.S. Obzor osnovnykh voprosov avtomatizirovannogo sostavleniya raspisaniya zanyatiy v vuze [An overview of the main issues of automated scheduling of classes in high school], Sovremennye nauchnye issledovaniya i innovatsii [Modern scientific research and innovation], 2011, № 5. Available at: http://web.snauka.ru/issues/2011/09/2576 (accessed: 20 February 2014).
19. Qu R. et al. A survey of search methodologies and automated system development for examination timetabling, Journal of scheduling, 2009, No. 12, pp. 55-89.

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