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Genetic programming may be more powerful
than neural networks and other machine learning techniques;
it may be able to solve problems in a wider range of disciplines.
In this groundbreaking book, John Koza shows how this remarkable
paradigm works and provides substantial empirical evidence
that solutions to a great variety of problems from many
different fields can be found by genetically breeding populations
of computer programs, Genetic Programming contains
many worked examples and includes a sample computer code
that will allow readers to run their own programs.
In getting computers to solve problems without
being explicitly programmed for them, Koza stresses two
points: that seemingly different problems from a variety
of fields can be reformulated as problems of program induction,
and that the recently developed genetic programming paradigm
provides a way to search the space of possible computer
programs for an individual program that is highly fit to
solve the problems of program induction. Good programs are
found by evolving them in a computer against a fitness measure
instead of by sitting down and writing them.
John R. Koza is Consulting Professor
in the Computer Science Department at Stanford University.
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