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Evolutionary multi-agent systems

  • Aleksander Byrski (a1), Rafał Dreżewski (a1), Leszek Siwik (a1) and Marek Kisiel-Dorohinicki (a1)

Abstract

The aim of this paper is to give a survey on the development and applications of evolutionary multi-agent systems (EMAS). The paper starts with a general introduction describing the background, structure and behaviour of EMAS. EMAS application to solving global optimisation problems is presented in the next section along with its modification targeted at lowering the computation costs by early removing certain agents based on immunological inspirations. Subsequent sections deal with the elitist variant of EMAS aimed at solving multi-criteria optimisation problems, and the co-evolutionary one aimed at solving multi-modal optimisation problems. Each variation of EMAS is illustrated with selected experimental results.

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References

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Bäck, T., Fogel, D. & Michalewicz, Z. (eds) 1997. Handbook of Evolutionary Computation, IOP Publishing and Oxford University Press.
Back, T., Hammel, U. & Schwefel, H.-P. 1997. Evolutionary computation: comments on the history and current state. IEEE Transactions on Evolutionary Computation 1(1), 317.
Bäck, T. & Schwefel, H.-P. 1996. Evolutionary computation: an overview. In Proceedings of the Third IEEE Conference on Evolutionary Computation, T. Fukuda & T. Furuhashi (eds), 2029. IEEE Press.
Bouvry, P., González-Vélez, H. & Kołodziej, J. 2011. Intelligent Decision Systems in Large-Scale Distributed Environments, Springer.
Bui, L. T., Essam, D., Abbas, H. A. & Green, D. 2004. Performance analysis of evolutionary multiobjective optimization methods in noisy environments. In 8th Asia Pacific Symposium on Intelligent and Evolutionary Systems, Monash University.
Byrski, A., Debski, R. & Kisiel-Dorohinicki, M. 2012. Agent-based computing in an augmented cloud environment. Computer Systems Science and Engineering 27(1), 520.
Byrski, A., Dobrowolski, J. & Toboła, K. 2008. Agent-based optimization of neural classifiers. In Conference on Evolutionary Computation and Global Optimization 2008, June 2–4.
Byrski, A. & Kisiel-Dorohinicki, M. 2007. Agent-based evolutionary and immunological optimization. In Proceedings of 7th International Conference on Computational Science – ICCS 2007. Springer, May 27–30.
Byrski, A., Kisiel-Dorohinicki, M. & Carvalho, M. 2010. A crisis management approach to mission survivability in computational multi-agent systems. Computer Science 11, 99113.
Cantú-Paz, E. 1995. A Summary of Research on Parallel Genetic Algorithms. IlliGAL Report No. 95007, University of Illinois.
Cetnarowicz, K. 1996. Evolution in multi-agent world = genetic algorithms + aggregation + escape. In 7th European Workshop on Modelling Autonomous Agents in a Multi-Agent World (MAAMAW’ 96). Vrije Universiteit Brussel, Artificial Intelligence Laboratory.
Cetnarowicz, K., Kisiel-Dorohinicki, M. & Nawarecki, E. 1996. The application of evolution process in multi-agent world (MAW) to the prediction system. In Proceedings of the 2nd International Conference on Multi-Agent Systems (ICMAS’96), M. Tokoro (ed.), 2632. AAAI Press.
Chen, S.-H., Kambayashi, Y. & Sato, H. 2011. Multi-Agent Applications with Evolutionary Computation and Biologically Inspired Technologies, IGI Global.
Coello Coello, C. A., Lamont, G. B. & Van Veldhuizen, D. A. 2007. Evolutionary Algorithms for Solving Multi-Objective Problems, 2nd edition. Kluwer Academic Publishers.
Dasgupta, D. & Nino, L. 2008. Immunological Computation Theory and Applications, Auerbach.
de Castro, L. N. 2006. Fundamentals of Natural Computing: Basic Concepts, Algorithms, and Applications. CRC Computer and Information Science Series. Chapman and Hall.
de Jong, K. 2002. Evolutionary Computation, A Bradford Book.
Deb, K. 2001. Multi-Objective Optimization Using Evolutionary Algorithms, John Wiley & Sons.
Digalakis, J. & Margaritis, K. 2002. An experimental study of benchmarking functions for evolutionary algorithms. International Journal of Computer Mathematics 79(4), 403416.
Dresner, K. & Stone, P. 2008. A multiagent approach to autonomous intersection management. Journal of Artificial Intelligence Research 31, 591656.
Dreżewski, R. 2003. A model of co-evolution in multi-agent system. In Multi-Agent Systems and Applications III, V. Mařík, J. Müller & M. Pĕchouček (eds), LNCS 2691, 314323. Springer-Verlag.
Dreżewski, R. 2006. Co-evolutionary multi-agent system with speciation and resource sharing mechanisms. Computing and Informatics 25(4), 305331.
Dreżewski, R. & Cetnarowicz, K. 2007. Sexual selection mechanism for agent-based evolutionary computation. In Computational Science – ICCS 2007, Y. Shi, G. D. van Albada, J. Dongarra & P. M. A. Sloot (eds), LNCS 4488, 920927. Springer-Verlag.
Dreżewski, R. & Siwik, L. 2010. A review of agent-based co-evolutionary algorithms for multi-objective optimization. In Computational Intelligence in Optimization. Application and Implementations, Springer-Verlag.
Fogel, D. B. 1998. Evolutionary Computation: The Fossil Record. Selected Readings on the History of Evolutionary Computation, IEEE Press.
Fonseca, C. M. & Fleming, P. J. 1995. An overview of evolutionary algorithms in multiobjective optimization. Evolutionary Computation 3(1), 116.
Franklyn, S. & Graesser, A. 1997. Is it an agent, or just a program?: a taxonomy for autonomous agents. In Intelligent Agents III: Agent Theories, Architectures and Languages. LNCS 1193/1997, 2135. Springer Verlag.
Fusinska, B., Kisiel-Dorohinicki, M. & Nawarecki, E. 2007. Coevolution of a fuzzy rule base for classification problems. In Rough Sets and Intelligent Systems Paradigms: International Conference, RSEISP 2007, LNCS/LNAI 4585, 678686. Springer.
George, J., Gleizes, M., Glize, P. & Regis, C. 2003. Real-time simulation for flood forecast: an adaptive multi-agent system staff. In Proceedings of the AISB’03 Symposium on Adaptive Agents and Multi-Agent Systems, University of Wales.
Horst, R. & Pardalos, P. 1995. Handbook of Global Optimization, Kluwer Academic Publishers.
Jennings, N., Faratin, P., Johnson, M., Norman, T., OBrien, P. & Wiegand, M. 1996. Agent-based business process management. International Journal of Cooperative Information Systems 5(2–3), 105130.
Kisiel-Dorohinicki, M. 2002. Agent-oriented model of simulated evolution. In SofSem 2002: Theory and Practice of Informatics, W. I. Grosky & F. Plasil (eds), LNCS 2540, 253261. Springer.
Lobel, B., Ozdaglar, A. & Feijer, D. 2011. Distributed multi-agent optimization with state-dependent communication. Mathematical Programming 129(2), 255284.
Mahfoud, S. W. 1992. Crowding and preselection revisited. In Parallel Problem Solving from Nature – PPSN-II, R.Männer & B. Manderick (eds), Elsevier, 2736.
Mahfoud, S. W. 1995. Niching Methods for Genetic Algorithms. PhD thesis, University of Illinois at Urbana-Champaign.
McArthur, S., Catterson, V. & Hatziargyriou, N. 2007. Multi-agent systems for power engineering applications. Part i: concepts, approaches, and technical challenges. IEEE Transactions on Power Systems 22(4), 17431752.
Moya, L. J. & Tolk, A. 2007. Towards a taxonomy of agents and multi-agent systems. In Proceedings of the 2007 Spring Simulation Multiconference – Volume 2, Society for Computer Simulation International, 11–18.
Paredis, J. 1995. Coevolutionary computation. Artificial Life 2(4), 355375.
Pietak, K., Wós, A., Byrski, A. & Kisiel-Dorohinicki, M. 2009. Functional integrity of multi-agent computational system supported by component-based implementation. In Proceedings of the 4th International Conference on Industrial Applications of Holonic and Multi-Agent Systems. Mařík, V., Strasser, T. & Zoitl, A. (eds), LNCS 5696, 82–91. Springer Berlin Heidelberg.
Potter, M. A. & De Jong, K. A. 2000. Cooperative coevolution: an architecture for evolving coadapted subcomponents. Evolutionary Computation 8(1), 129.
Russell, S. J. & Norvig, P. 2009. Artificial Intelligence: A Modern Approach, 3rd edition. Prentice Hall.
Sánchez-Velazco, J. & Bullinaria, J. A. 2003. Gendered selection strategies in genetic algorithms for optimization. In Proceedings of the UK Workshop on Computational Intelligence (UKCI 2003), J. M. Rossiter & T. P. Martin (eds), University of Bristol, 217223.
Sarker, R. & Ray, T. 2010. Agent-Based Evolutionary Search (Adaptation, Learning and Optimization), vol. 5, 1st edition. Springer.
Schaefer, R., Byrski, A. & Smołka, M. 2009. Stochastic model of evolutionary and immunological multi-agent systems: parallel execution of local actions. Fundamenta Informaticae 95(2–3), 325348.
Schaefer, R. & Kołodziej, J. 2003. Genetic search reinforced by the population hierarchy. Foundations of Genetic Algorithms 7, 383399.
Siwik, L. & Dreżewski, R. 2009. Agent-based multi-objective evolutionary algorithms with cultural and immunological mechanisms. In Evolutionary Computation, W. P. dos Santos (ed.), InTech, 541556.
Siwik, L. & Natanek, S. 2008. Solving constrained multi-criteria optimization tasks using elitist evolutionary multi-agent system. In Proceedings of 2008 IEEE World Congress on Computational Intelligence (WCCI 2008), 2008 IEEE Congress on Evolutionary Computation (CEC 2008). IEEE Research Publishing Services, 3357–3364.
Uhruski, P., Grochowski, M. & Schaefer, R. 2008. A two-layer agent-based system for large-scale distributed computation. Computational Intelligence 24(3), 191212.
Van Veldhuizen, D. A. 1999. Multiobjective Evolutionary Algorithms: Classifications, Analyses and New Innovations, PhD thesis, Graduate School of Engineering, Air Force Institute, Technology Air University.
Veldhuizen, D. A. V. & Lamont, G. B. 2000. Multiobjective evolutionary algorithms: analyzing the state-of-the-art. Evolutionary Computation 8(2), 125147.
Wierzchoń, S. 2002. Function optimization by the immune metaphor. Task Quarterly 6(3), 116.
Wolpert, D. H. & Macready, W. G. 1997. No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation 1(1), 6782.
Wooldridge, M. 2009. An Introduction to Multiagent Systems, John Wiley & Sons.
Zhong, W., Liu, J., Xue, M. & Jiao, L. 2004. A multiagent genetic algorithm for global numerical optimization. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 34(2), 11281141.
Zitzler, E. 1999. Evolutionary Algorithms for Multiobjective Optimization: Methods and Applications. PhD thesis, Swiss Federal Institute of Technology.

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Evolutionary multi-agent systems

  • Aleksander Byrski (a1), Rafał Dreżewski (a1), Leszek Siwik (a1) and Marek Kisiel-Dorohinicki (a1)

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