abstract = "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.
The book also includes chapters on the dynamics of GP,
the selection of operators and population sizing,
specific applications such as stock selection in
emerging markets, predicting oil field production,
modeling chemical production processes, and developing
new diagnostics from genomic data.
Genetic Programming Theory and Practice is an excellent
reference for researchers working in evolutionary
algorithms and for practitioners seeking innovative
methods to solve difficult computing problems.",