Summary
Co-evolutionary techniques for evolutionary algorithms can enhance the adaptive capabilities of evolutionary algorithms and help maintain population diversity. In this chapter the concept and a formal model of an agent-based realization of a predator-prey coevolutionary algorithm is presented. The resulting system is applied to the problem of effective portfolio building and is compared to classical multi-objective evolutionary algorithms.
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Keywords
- Multiobjective Optimization
- Portfolio Optimization
- Pareto Frontier
- Capital Asset Price Model
- Strength Pareto Evolutionary Algorithm
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References
Adamidis P (1998) Parallel evolutionary algorithms: A review. In: Proceedings of the 4th Hellenic-European Conference on Computer Mathematics and its Applications (HERCMA 1998), Athens, Greece
Allenson R (1992) Genetic algorithms with gender for multi-function opti-misation. Tech. Rep. EPCC-SS92-01, Edinburgh Parallel Computing Centre, Edinburgh, Scotland
Bäck T, Fogel D, Michalewicz Z (eds) (1997) Handbook of Evolutionary Com-putation. IOP Publishing and Oxford University Press
Bonissone S, Subbu R (2003) Exploring the pareto frontier using multi-sexual evolutionary algorithms: An application to a flexible manufacturing problem. Tech. Rep. 2003GRC083, GE Global Research
Cantú-Paz E (1998) A survey of parallel genetic algorithms. Calculateurs Paralleles, Reseaux et Systems Repartis 10(2):141-171
Darwen PJ, Yao X (1995) On evolving robust strategies for iterated prisoner's dilemma. In: Yao X (ed) Process in Evolutionary Computation, AI'93 and AI'94 Workshops on Evolutionary Computation, Selected Papers, Springer-Verlag, LNCS, vol 956
Deb K (2001) Multi-Objective Optimization using Evolutionary Algorithms. John Wiley & Sons
Dreżewski R(2003) A model of co-evolution in multi-agent system. In: Marík V, Müller J, Pĕchouček M(eds) Multi-Agent Systems and Applications III, Springer-Verlag, Berlin, Heidelberg, LNCS, vol 2691, pp 314-323, http://galaxy.uci.agh.edu.pl/∼rezew/publications/drezewski2003model.pdf
Dreżewski R, Siwik L (2006) Co-evolutionary multi-agent system with sexual selection mechanism for multi-objective optimization. In: Proceedings of the IEEE World Congress on Computational Intelligence (WCCI 2006), IEEE
Dreżewski R, Siwik L (2006) Multi-objective optimization using co-evolutionary multi-agent system with host-parasite mechanism. In: AlexandrovVN, van Albada GD, Sloot PMA, Dongarra J (eds) Computational Science — ICCS 2006, Springer-Verlag, Berlin, Heidelberg, LNCS, vol 3993, pp 871-878
Dreżewski R, Siwik L (2007) Co-evolutionary multi-agent system with predator-prey mechanism for multi-objective optimization. In: BeliczynskiB, Dzielinski A, Iwanowski M, Ribeiro B (eds) Adaptive and Natural Computing Algorithms, Springer-Verlag, LNCS, vol 4431, pp 67-76
Dreżewski R, Siwik L (2007) Multi-objective optimization technique based on co-evolutionary interactions in multi-agent system. In: GiacobiniM (ed) Ap-plications of Evolutionary Computing, Springer-Verlag, LNCS, vol 4448, pp 179-188
Ferber J (1999) Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence. Addison-Wesley
Fonseca C, Fleming P (1993) Genetic algorithms for multiobjective optimization: Formulation, discussion and generalization. In: Genetic Algorithms: Proceedings of the Fifth International Conference, Morgan Kaufmann, pp 416-423, citeseer.ist.psu.edu/fonseca93genetic.html
Gavrilets S (2003) Models of speciation: what have we learned in 40 years? Evolution 57(10):2197-2215
Goldberg DE, Richardson J (1987) Genetic algorithms with sharing for multi-modal function optimization. In: Grefenstette JJ (ed) Proceedings of the 2nd In-ternational Conference on Genetic Algorithms, Lawrence Erlbaum Associates, pp 41-49
Hajela P, Lin C (1992) Genetic search strategies in multicriterion optimal design. In: Structural optimization 4, pp 99-107
Horn J, Nafpliotis N, Goldberg DE (1994) A niched pareto genetic algorithm for multiobjective optimization. In: Proceedings of the First IEEE Conference on Evolutionary Computation, IEEE Service Center, Piscataway, New Jersey, pp 82-87
Iorio A, Li X (2004) A cooperative coevolutionary multiobjective algorithm using non-dominated sorting. In: Deb K, Poli R, Banzhaf W, Beyer HG, Burke EK, Darwen PJ, Dasgupta D, Floreano D, Foster JA, Harman M, Holland O, Lanzi PL, Spector L, Tettamanzi A, Thierens D, Tyrrell AM (eds) Genetic and Evolutionary Computation - GECCO 2004, Springer-Verlag, LNCS, vol 3102-3103, pp 537-548
Jelasity M, Dombi J (1998) GAS, a concept of modeling species in genetic algorithms. Artificial Intelligence 99:1-19
Kursawe F (1991) A variant of evolution strategies for vector optimization. In: Schwefel H, Manner R (eds) Parallel Problem Solving from Nature. 1st Workshop, PPSN I, Springer-Verlag, Berlin, Germany, vol 496, pp 193-197, citeseer.ist.psu.edu/kursawe91variant.html
Laumanns M, Rudolph G, Schwefel HP (1998) A spatial predator-prey ap-proach to multi-objective optimization: A preliminary study. In: EibenAE, Bäck T, Schoenauer M, Schwefel HP (eds) Parallel Problem Solving from Na-ture — PPSN V, Springer-Verlag, LNCS, vol 1498
Li X (2003) A real-coded predator-prey genetic algorithm for multiobjective optimization. In:Fonseca CM, Fleming PJ, Zitzler E, Deb K, Thiele L (eds) Evolutionary Multi-Criterion Optimization, Second International Conference (EMO 2003), Proceedings, Springer-Verlag, LNCS, vol 2632
Lintner J (1965) The valuation of risk assets and the selection of risky investments in stock portfolios and capital budgets. Review of Economics and Statistics 47:13-37
Lis J, Eiben AE (1996) A multi-sexual genetic algorithm for multiobjective optimization. In: Fukuda T, Furuhashi T (eds) Proceedings of the Third IEEE Conference on Evolutionary Computation, IEEE Press, Piscataway NJ, pp 59-64
Mahfoud SW (1992) Crowding and preselection revisited. In: MännerR, ManderickB (eds) Parallel Problem Solving from Nature — PPSN-II, Elsevier, Amsterdam, pp 27-36, illiGAL report No. 92004
Mahfoud SW (1995) Niching methods for genetic algorithms. PhD thesis, University of Illinois at Urbana-Champaign, Urbana, IL, USA, citeseer.nj.nec.com/mahfoud95niching.html
Markowitz H (1952) Portfolio selection. Journal of Finance 7(1):77-91
Markowitz H (1999) The early history of portfolio theory: 1600-1960. Financial Analysts Journal 55(4):5-16
Paredis J (1995) Coevolutionary computation. Artificial Life 2(4):355-375
Paterson R (2002) Compendium of Banking Terms in Polish and English. Foundation of accountancy development in Poland, Warsaw
Potter MA, De Jong KA (2000) Cooperative coevolution: An architecture for evolving coadapted subcomponents. Evolutionary Computation 8(1):1-29
Ratford M, Tuson AL, Thompson H (1997) An investigation of sexual selection as a mechanism for obtaining multiple distinct solutions. Tech. Rep. 879, Department of Artificial Intelligence, University of Edinburgh
Rom B, Ferguson K (1993) Post-modern portfolio theory comes of age. The Journal of Investing Winter
Ross S (1976) The arbitrage theory of capital asset pricing. Journal of Economic Theory 13(3)
Sharpe WF (1964) Capital asset prices: A theory of market equilibrium under conditions of risk. Journal of Finance 19(3):425-442
Srinivas N, Deb K (1994) Multiobjective optimization using nondominated sorting in genetic algorithms. Evolutionary Computation 2(3):221-248
Tobin J (1958) Liquidity preference as behavior towards risk. The Review of Economic Studies 25:65-86
Todd PM, Miller GF (1997) Biodiversity through sexual selection. In: Ch G Langton, et al (ed) Artificial Life V: Proceedings of the Fifth Int. Workshop on the Synthesis and Simulation of Living Systems, Bradford Books, pp 289-299
Treynor J (1961) Towards a theory of market value of risky assets. unpublished manuscript
Zitzler E(1999) Evolutionary algorithms for multiobjective optimization: methods and applications. PhD thesis, Swiss Federal Institute of Technology, Zurich
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Dreżewski, R., Siwik, L. (2008). Co-Evolutionary Multi-Agent System for Portfolio Optimization. In: Brabazon, A., O’Neill, M. (eds) Natural Computing in Computational Finance. Studies in Computational Intelligence, vol 100. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77477-8_15
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