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There is increasing interest in genetic programming by both researchersand professional software developers. These twenty-two invited contributions showhow a wide variety of problems across disciplines can be solved using this newparadigm.Advances in Genetic Programming reports significant results in improvingthe power of genetic programming, presenting techniques that can be employedimmediately in the solution of complex problems in many areas, including machinelearning and the simulation of autonomous behavior. Popular languages such as C andC++ are used in many of the applications and experiments, illustrating how geneticprogramming is not restricted to symbolic computing languages such as LISP.Researchers interested in getting started in genetic programming will findinformation on how to begin, on what public domain code is available, and on how tobecome part of the active genetic programming community via electronic mail.A majorfocus of the book is on improving the power of genetic programming. Experimentalresults are presented in a variety of areas, including adding memory to geneticprogramming, using locality and "demes" to maintain evolutionary diversity, avoidingthe traps of local optima by using coevolution, using noise to increase generality, and limiting the size of evolved solutions to improve generality.Significanttheoretical results in the understanding of the processes underlying geneticprogramming are presented, as are several results in the area of automatic functiondefinition. Performance increases are demonstrated by directly evolving machinecode, and implementation and design issues for genetic programming in C++ arediscussed.
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