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Bellas, R J Duro, Introducing Long Term Memory in an ANN-Based Multilevel Darwinist Brain, Lecture Notes in Computer Science, Vol 2686, 2003, pp 590 598 22 F Bellas, J A Becerra, R J Duro, Construction of a Memory Management System in an On-line Learning Mechanism ESANN 2006 Proceedings Book, 2006 23 X Yao, Evolving Arti cial Neural Networks Proc IEEE, 87(9), 1999, 1423 1447 24 K Trojanowski, Z Michalewicz, Evolutionary Approach to Non-stationary Optimisation Tasks Lecture Notes in Computer Science, Vol 1609, 1999, pp 538 546 25 N Mori, H Kita, Y Nishikawa, Adaptation to a Changing Environment by Means of the Feedback Thermodynamical Genetic Algorithm LNCS, Vol 1498, pp 149 158, 1998 26 D Dasgupta, D R MacGregor, Nonstationary Function Optimization Using the Structured Genetic Algorithm Proc Parallel Problem Solving from Nature 2, 1992, pp 145 154 27 C Ryan, J J Collins, D.





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Wallin, Non-stationary Function Optimization Using Polygenic Inheritance Lecture Notes in Computer Science, Vol 2724, pp 1320 1331, 2003 28 J Eggermont, T Lenaerts, Non-stationary Function Optimization Using Evolutionary Algorithms with a Case-Based Memory, Technical Report TR 2001-11, 2001 29 T D Silva, R Janik, M Chrien, K Stanley, R Miikkulainen, Retaining Learned Behavior During Real-Time Neuroevolution Proc AIIDE 2005, 2005 30 H G Cobb, An Investigation into the Use of Hypermutation as an Adaptive Operator in Genetic Algorithms Having Continuous, Time-Dependent Nonstationary Environments, NRL Memorandum Report 6760, pp 523 529, 1990 31 D E Moriarty, Symbiotic Evolution of Neural Networks in Sequential Decision Tasks, PhD Thesis, University of Texas at Austin, Tech Rep UT-AI97-257, 1997 32 F Gomez, D Burger, R Miikkulainen, A Neuroevolution Method for Dynamic Resource Allocation on a Chip Multiprocessor Proc Int Joint Conf on Neural Networks, 2001 33 K O Stanley, R.

fullband filter length M in powers of 2 between 32 and 1024. We see that in this case, the ratio drops below one for M larger than or equal to 64.

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Miikkulainen, Evolving Neural Networks through Augmenting Topologies, Evolutionary Computation, 10, pp 99 127, 2002 34 D Dasgupta, D McGregor, Designing Application-Speci c Neural Networks Using the Structured Genetic Algorithm Proc COGANN-92, 1992, pp 87 96 35 F Bellas, R J Duro, Modelling the World with Statiscally Neutral PBGAs: Enhancement and Real Applications Proc 9th Int Conf on Neural Information Processing, Vol 1, 2002, IEEE Press, pp 2093 2098..

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patterned regularity of interpersonal behavior, stating interpersonal re exes tend (with a probability greater than chance) to initiate or invite reciprocal interpersonal responses from the other person in the interaction that lead to a repetition of the original re ex (p 123) Learning in interpersonal situations takes place in part because social interaction is reinforcing (Leary, 1957) Carson (1991) referred to this as an interbehavioral contingency process whereby there is a tendency for a given individual s interpersonal behavior to be constrained or controlled in more or less predictable ways by the behavior received from an interaction partner (p 191) Describing Reciprocal Interpersonal Patterns Structural models of interpersonal behavior such as the IPC and SASB have provided conceptual anchoring points and lexicons upon which more systematic description of the patterned regularity of reciprocal interpersonal processes can be articulated (eg, Benjamin, 1974; Carson, 1969; Kiesler, 1983).

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