An agent-based model of hierarchic genetic search

Computers and Mathematics with Applications

Volumn 64, Issue 12, 2012, Pages 3763-3776

  Schaefer, Robert ^a   Byrski, Aleksander ^a   Koodziej, Joanna ^b   Smoka, MacIej ^c  

^a AGH UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Poland)

^b UNIVERSITY OF BIELSKO BIALA   (Poland)

^c JAGIELLONIAN UNIVERSITY   (Poland)

Author keywords

Genetic algorithms; Global optimisation; Hierarchic genetic strategy; Multi agent genetic system

Indexed keywords

AGENT-BASED MODEL; COMPLEX PROCESSES; DECENTRALISED; DYNAMIC CHANGES; EVOLUTIONARY TECHNIQUES; FORMAL MODEL; GENETIC PROCESS; GENETIC SEARCH; GENETIC SYSTEMS; GLOBAL OPTIMISATION; HIERARCHIC GENETIC STRATEGY; MARKOVIAN; META HEURISTICS; METAHEURISTIC; MULTI AGENT SYSTEM (MAS); PARALLELISATION; PARAMETER SETTING; POPULATION MANAGEMENT; SEARCH ACCURACY; SEARCH PROCESS; SEARCH SPACES; STRUCTURE MANAGEMENT; SYSTEM STATE; TREE STRUCTURES;

GENETIC ALGORITHMS; GLOBAL OPTIMIZATION; MATHEMATICAL MODELS; MERCURY COMPOUNDS; MULTI AGENT SYSTEMS; TREES (MATHEMATICS);

FORESTRY;

ALGORITHMS; FORESTRY; GENETIC ENGINEERING; MATHEMATICAL MODELS; MERCURY COMPOUNDS; OPTIMIZATION;

EID: 84870254105     PISSN: 08981221     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.camwa.2012.02.052     Document Type: Article

References (22)

1. A. Byrski, and R. Schaefer Stochastic model of evolutionary and immunological multi-agent systems: mutually exclusive actions Fundamenta Informaticae 95 2-3 2009 263 285
2. R. Schaefer, A. Byrski, and M. Smoka Stochastic model of evolutionary and immunological multi-agent systems: parallel execution of local actions Fundamenta Informaticae 95 2-3 2009 325 348
3. M. Carvalho, and C. Perez An evolutionary multi-agent approach to anomaly detection and cyber defense CSIIRW'11: Proceedings of the 7th Annual Workshop on Cyber Security and Information Intelligence Research 2011 ACM
4. W.D. Whitley, S.B. Rana, and R.B. Heckendorn Island model genetic algorithms and linearly separable problems Selected Papers from AISB Workshop on Evolutionary Computing 1997 Springer-Verlag London, UK 109 125
5. R. Schaefer, and J. Koodziej Genetic search reinforced by the population hierarchy K.D. Jong, Foundations of Genetic Algorithms VII 2003 Morgan Kaufmann 369 385
6. J. Koodziej, W. Jakubiec, M. Starczak, and R. Schaefer Hierarchical genetic strategy applied to the problem of the coordinate measuring machine geometrical errors T. Burczyski, IUTAM'02 Symposium on Evolutionary Methods in Mechanics 24-27 September 2002 2004 Kluwer Ac. Press Cracow, Poland 22 30
7. J. Koodziej, and F. Xhafa Enhancing the genetic-based scheduling in computational grids by a structured hierarchical population Future Generation Computer Systems 27 2011 1035 1045
8. N.N. Schraudolph, and R.K. Belew Dynamic parameter encoding for genetic algorithms Machine Learning 9 1992 9 21
9. D. Whitley, K. Mathias, and P. Fitzhorn Delta coding: an iterative search strategy for genetic algorithms R. Belew, L. Booker, Proc. of the 4th International Conference on Genetic Algorithms 1991 Morgan Kaufmann 77 84
10. M. Srinivas, and L. Patnaik Adaptive probabilities of crossover and mutation in genetic algorithms IEEE Transactions on Systems, Man and Cybernetics 24 1994 656 667
11. J. Zhang, H. Chung, and W. Lo Clustering-based adaptive crossover and mutation probabilities for genetic algorithms IEEE Transactions on Evolutionary Computation 11 2007 326 335 (Pubitemid 46895411)
12. X. Yueju, L. Shuguang, Y. Jingfeng, and C. Qiang Genetic algorithm based adaptive neural network ensemble and its application in predicting carbon flux Proc. of the 3rd International Conference on Natural Computation ICNC 207 2007 IEEE Press 183 187
13. R. Schaefer, and H. Telega Foundation of Global Genetic Optimization Studies in Computational Intelligence vol. 74 2007 Springer Verlag Berlin, Heidelberg, New York
14. A.E. Nix, and M.D. Vose Modeling genetic algorithms with Markov chains Annals of Mathematics and Artificial Intelligence 5 1992 79 88
15. L.M. Schmitt Theory of genetic algorithm Theoretical Computer Science 259 2001 1 61
16. M. Vose The Simple Genetic Algorithm: Foundations and Theory 1998 MIT Press Cambridge, MA, USA
17. P. Billingsley Probability and Measure 1995 Wiley-Interscience
18. K. Cetnarowicz, M. Kisiel-Dorohinicki, and E. Nawarecki The application of evolution process in multi-agent world (MAW) to the prediction system M. Tokoro, Proc. of the 2nd Int. Conf. on Multi-Agent Systems ICMAS'96 1996 AAAI Press
19. A. Byrski, R. Schaefer, M. Smoka, and C. Cotta Asymptotic analysis of computational multi-agent systems R. Schaefer, C. Cotta, J. Koodziej, G. Rudolph, Proceedings of 11th International Conference on Parallel Problem Solving from Nature - PPSN XI LNCS vol. 6238 2011 Springer-Verlag 475 484
20. A. Byrski, M. Kisiel-Dorohinicki, and M. Carvalho A crisis management approach to mission survivability in computational multi-agent systems Computer Science 11 2010 99 113
21. A. Silberschatz, P.B. Galvin, and G. Gagne Operating System Concepts with Java eighth ed. 2010 Wiley
22. J. Momot, K. Kosacki, M. Grochowski, P. Uhruski, and R. Schaefer Multi-agent system for irregular parallel genetic computations W.I. Grosky, F. Plasil, SofSem 2002: Theory and Practice of Informatics LNCS vol. 3038 2004 Springer Verlag 623 630