Genetic Programming Theory and Practice explores the emerging
interaction between theory and practice in the cutting-edge,
machine learning method of Genetic Programming (GP). The material
contained in this contributed volume was developed from a workshop
at the University of Michigan's Center for the Study of Complex
Systems where an international group of genetic programming
theorists and practitioners met to examine how GP theory informs
practice and how GP practice impacts GP theory. The contributions
cover the full spectrum of this relationship and are written by
leading GP theorists from major universities, as well as active
practitioners from leading industries and businesses. Chapters
include such topics as John Koza's development of human-competitive
electronic circuit designs; David Goldberg's application of
"competent GA" methodology to GP; Jason Daida's discovery of a new
set of factors underlying the dynamics of GP starting from applied
research; and Stephen Freeland's essay on the lessons of biology
for GP and the potential impact of GP on evolutionary theory.
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