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Abstract: Sustained progress in artificial evolution is dependent on the ability of the variation process--via the transmission function [1], to continue to generate individuals of higher fitness. However, the design of a transmission function to yield good performance is a difficult task, and becomes increasingly problematic in complex, open-ended domains such as those explored in the field of Artificial Life. It is therefore an attractive proposition to allow the transmission function to adapt to the... (Update)
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BibTeX entry: (Update)
@misc{ glickman-offline,
author = "Matthew R. Glickman and Katia P. Sycara",
title = "Off-line Learning of a Domain-Specific Transmission Function for Artificial
Evolution",
url = "citeseer.ist.psu.edu/143239.html" }
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