- author = "John R. Koza and David Andre and Forrest H {Bennett III} and Martin Keane",
- title = "Genetic Programming III: {Darwinian} Invention and Problem Solving",
- publisher = "Morgan Kaufmann",
- year = "1999",
- month = apr,
- keywords = "genetic algorithms, genetic programming",
- ISBN = "1-55860-543-6",
-
URL = "
http://www.genetic-programming.org/gpbook3toc.html",
-
URL = "http://www.amazon.com/exec/obidos/ASIN/1558605436/geneticprogrammi",
-
video_url = "https://youtu.be/8DY7akqFvfw",
-
video_url = "http://www.human-competitive.org/sites/default/files/video/genetic_programming_3_human_competitive_machine_intelligence.mp4",
-
video_url = "http://gpbib.cs.ucl.ac.uk/koza_videos/genetic_programming_3_human_competitive_machine_intelligence.mp4",
- size = "1100 pages",
-
notes = "Genetic programming is a method for getting a computer
to solve a problem by telling it what needs to be done
instead of how to do it. The authors present
genetically evolved solutions to dozens of problems of
design, optimal control, classification, system
identification, function learning, and computational
molecular biology. Among the solutions are 14 results
competitive with human-produced results, including 10
rediscoveries of previously patented
inventions.
Researchers in artificial intelligence, machine learning, evolutionary computation, and genetic algorithms will find this an essential reference to the most recent and most important results in the rapidly growing field of genetic programming.
FEATURES
-- Explains how the success of genetic programming arises from seven fundamental differences distinguishing it from conventional approaches to artificial intelligence and machine learning
-- Describes how genetic programming uses architecture-altering operations to make on-the-fly decisions on whether to use subroutines, loops, recursions, and memory
-- Demonstrates that genetic programming possesses 16 attributes that can reasonably be expected of a system for automatically creating computer programs
-- Presents the general-purpose Genetic Programming Problem Solver
-- Includes an introduction to genetic programming for the uninitiated
CONTENTS
Introduction -- Background -- Architecture-Altering Operations -- Genetic Programming Problem Solver (GPPS) -- Automated Synthesis of Analog Electrical Circuits -- Evolvable Hardware -- Discovery of Cellular Automata Rules -- Discovery of Motifs and Programmatic Motifs for Molecular Biology -- Parallelization and Implementation Issues -- Conclusion.
Memory-efficient crossover Section 63.4 pages 1044-1045. For generational GA memory required = M+2 (M = population size)
Koza videos \cite{koza:video} \cite{koza:video2} \cite{koza:video3} \cite{koza:video4} \cite{koza:2009:gpt}",
