Created by W.Langdon from gp-bibliography.bib Revision:1.7546
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.
-- 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
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)
Genetic Programming entries for John Koza David Andre Forrest Bennett Martin A Keane