Abstract
The use of evolutionary computing techniques in coevolutionary/multiagent systems is becoming increasingly popular. This paper presents some simple models of the genetic algorithm in such systems, with the aim of examining the effects of different types of interdependence between individuals. Using the models, it is shown that for a fixed amount of interdependence between homogeneous coevolving individuals, the existence of partner gene variance, gene symmetry, and the level at which fitness is applied can have significant effects. Similarly, for heterogeneous coevolving systems with fixed interdependence, partner gene variance and fitness application are also found to have a significant effect, as is the partnering strategy used.
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References
Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor
Barricelli NA (1957) Symblogenetic evolution processes realised by artificial methods. Methods 9:35–36
Axlerod R (1987) Evolution of strategies in the iterated prisoner's dilemma. In: Davis L (ed) Genetic algorithms and simulated annealing. Morgan Kaufmann, Los Altos, p 32–41
Hillis WD (1991) Coevolving parasites improve simulated evolution as an optimization procedure. Physica D 42:228–234
Beer RD, Gallagher JC (1992) Evolving dynamical neural networks for adaptive behaviour. Adapt Behav 1:91–122
Miller G, Cliff D (1994) Protean behaviour in dynamic games. In: Cliff D, Husbands P, Meyer J-A, Wilson SW (eds) From animals to animals, vol 3 MIT Press, Cambridge p 411–420
Floreano D, Nolfi S (1997) Adaptive behaviour in competing coevolving species. In: Husbands P, Harvey I (eds) Proceedings of the 4th European Conference on Artificial Life. MIT Press, Cambridge, p. 378–387
Husbands P, Mill F (1991) Simulated coevolution as the mechanism for emergent planning and scheduling. In: Belew RK, Booker LB (eds) Proceedings of the 4th International Conference on Genetic Algorithms. Morgan Kaufmann, Los Altos, p 264–270
Goldberg DE (1987) Simple genetic algorithms and the minimal, deceptive problem. In: Davis L (ed) Genetic algorithms and simulated annealing. Morgan Kaufmann, Los Altos, p 74–88
Kauffman SA, Johnsen S (1991) Coevolution to the edge of chaos: coupled fitness landscapes, poised states, and coevolutionary avalanches. In: Langton CG, Taylor C, Farmer JD, Rasmussen S (eds) Artificial life II. Addision-Wesley, Reading, p 325–370
Bull L, Fogarty TC (1994) Evelving cooperative communicating classifier systems. In: Sebald AV, Fogel LJ (eds) Proceedings of the 3rd Annual Conference on Evolutioary Programming. World Scientific, p 308–315
Fogel DB (1993) Evolving behaviours in the iterated prisoner's dilemma Evol Comput 1:77–97
Angeline P, Pollack J (1993) Competitive environments evolve better solutions for complex tasks. In: Forrest S (ed) Proceedings of the 5th International Conference on Genetic Algorithms. Morgan Kaufmann, Los Altos, p. 264–270
Koza JR (1992) Genetic programming. MIT Press, Cambridge.
Reynolds CW (1994) Competition, coevolution and the game of tag. In: Brooks R, Maes P (eds) Artificial life IV. MIT Press, Cambridge, p 59–69
White T, Pagurek B, Oppacher F (1998) ASGA: improving the ant system by integration with genetic algorithms. In: Koza JR, Banzhaf W, Chellapilla K, Deb K, Fogel DB, Garzon M, Goldberg DE, Iba H, Riolo RL (eds) Proceedings of the 3rd Annual Genetic Programming Conference. Morgan Kaufmann, Los Altos, p 610–616
Sipper M (1994) Non-uniform cellular automata: evolution in rule space and formation of complex structures. In: Brooks R, Maes P (eds) Artificial life IV. MIT Press, Los Altos, p 394–399
Unemi T, Nagoyshi M, Hirayama N, et al. (1994) Evolutionary differentiation of learning abilities: a case study on optimizing parameter values in Q-learning by a genetic alogrithm. In: Brooks R, Maes P (eds) Artificial life IV. MIT Press, Cambridge, p 331–336
Dorigo M, Maniezzo V, Colorni A (1996) The ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern 26:29–41
Bull L, Fogarty TC, Snaith M (1995) Evolution in multi-agent systems: evolving communicating classifier systems for gait in a quadrupedal robot. In: Eshelman LJ (ed) Proceedings of the 6th International Conference on Genetic Algorithms. Morgan Kaufmann, Los Altos, p 382–388
Bull L (1997) Evolutionary computing in multi-agent environments: partners. In: T Baeck (ed) Proceedings of the 7th International Conference on Genetic Algorithms. Morgan Kaufmann Los Altos, p 370–377
Iba H (1996) Emergent cooperation for multiple agents using genetic programming. In: Voigt H-M, Ebeling W, Rechenberg I, Schwefel H-P (eds) Parallel problem solving from nature, PPSN IV. Springer, p 32–41
Parmee I (1996) The development of a dual-agent strategy for efficient search across whole system engineering design hierarchies. In: Voigt H-M, Ebeling W, Rechenberg I, Schwefel H-P (eds) Parallel problem solving from nature, PPSN IV. Springer, p 523–532
Ahluwalia M, Bull L (1998) Coevolving functions in genetic programming: dynamic ADF creation using GLiB. In: Porto VW, Saravanan N, Wagen D, Eiben AE (eds) Proceedings of the 7th Annual Conference on Evolutionary Programming. Springer, p 809–818
Paredis J (1994) Steps towards coevolutionary classification neural networks. In: Brooks RA, Maes P (eds) Artificial life IV. MIT Press, Cambridge, p 102–108
Bull L (1998) On evolutionary computing in multi-agent environments: operators. In: Porto VW, Saravanan N, Waagen G, Eiben AE (eds) Proceedings of the 7th Annual Evolutionary Programming Conference. Springer, p 43–52
Nix AE, Vose MD (1992) Modelling genetic algorithms with Markov chains. Ann Math Artif Intell 5:79–88
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Bull, L. Simple models of coevolutionary genetic algorithms. Artif Life Robotics 5, 58–66 (2001). https://doi.org/10.1007/BF02481321
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DOI: https://doi.org/10.1007/BF02481321