D. W. Hillis. Co-evolving parasites improve simulated evolution in an optimization procedure. Physica D, 42:228--234, 1990. Home/Search Document Not in Database Summary Related Articles Check |
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....see [11] This coevolutionary work has largely concentrated on competitive interactions. The interactions can be between individuals that compete in a symmetric game like context [12] 14] or between populations of di#erent types of individuals that compete in predator prey type relationships [4], 9] 8] 5] 13] In these cases, individuals are rewarded if they defeat the individuals with which they compete. These interactions can support arms races in which the individuals force each other to become increasingly competent. A few studies have investigated the role of cooperation ....
D. W. Hillis, Co-evolving parasites improve simulated evolution as an optimization procedure. Pages 313-324 of: Langton, C., Taylor, C., Farmer, J. D., Rasmussen, S. (eds), Artificial life 2, vol. X. Redwood City, CA: AddisonWesley, 1991.
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....opponents, and the evaluation function becomes more discerning. The aim is to set up an escalating arms race of innovation. Co evolution has also worked on Checkers [6] Backgammon [7] 20] and on non game tasks [17] 19] 21] such as scheduling [13] 15] and creating a sorting algorithm [14]. 2 Experimental Setup 2.1 Feed Forward Neural Network Representation Intermediate choices would make for a cumbersome look up table. A more convenient way to represent an IPD strategy is as a feed forward neural network. Each member of the population is a fixed length array of floating point ....
W. Daniel Hillis. Co-evolving parasites improve simulated evolution as an optimization procedure. In Artificial Life 2, pages 313--323. Addison-Wesley, 1991.
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....capability on unseen data (e.g. 8] but in general this cannot be guaranteed. This chapter s approach draws inspiration from several sources. For instance, the two module system is based on two co evolving modules. Coevolution of competing strategies, however, is nothing new. See, for example, [7,19] for interesting cases. Also, the idea of improving a learner by letting it play against itself is ancient. See, for example, 20,41] Even the idea of unsupervised learning through co evolution of predictors and modules trying to escape the predictions is nothing new it has been used ....
D. Hillis. Co-evolving parasites improve simulated evolution as an optimization procedure. In C. G. Langton, C. Taylor, J. D. Farmer, and S. Rasmussen, editors, Arti cial Life II, pages 313-324. Addison Wesley, 1992.
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D. W. Hillis. Co-evolving parasites improve simulated evolution in an optimization procedure. Physica D, 42:228--234, 1990.
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