Advances in Genetic Programming: v.1

There is increasing interest in genetic programming by both researchers
and professional software developers. These twenty-two invited contributions show
how a wide variety of problems across disciplines can be solved using this new
paradigm.Advances in Genetic Programming reports significant results in improving
the power of genetic programming, presenting techniques that can be employed
immediately in the solution of complex problems in many areas, including machine
learning and the simulation of autonomous behavior. Popular languages such as C and
C++ are used in many of the applications and experiments, illustrating how genetic
programming is not restricted to symbolic computing languages such as LISP.
Researchers interested in getting started in genetic programming will find
information on how to begin, on what public domain code is available, and on how to
become part of the active genetic programming community via electronic mail.A major
focus of the book is on improving the power of genetic programming. Experimental
results are presented in a variety of areas, including adding memory to genetic
programming, using locality and "demes" to maintain evolutionary diversity, avoiding
the traps of local optima by using coevolution, using noise to increase generality,
and limiting the size of evolved solutions to improve generality.Significant
theoretical results in the understanding of the processes underlying genetic
programming are presented, as are several results in the area of automatic function
definition. Performance increases are demonstrated by directly evolving machine
code, and implementation and design issues for genetic programming in C++ are
discussed.