@InProceedings{Koza89,
author = "J. R. Koza",
title = "Hierarchical genetic algorithms operating on
populations of computer programs",
editor = "N. S. Sridharan",
volume = "1",
pages = "768774",
booktitle = "Proceedings of the Eleventh International Joint
Conference on Artificial Intelligence IJCAI89",
year = "1989",
keywords = "genetic algorithms, genetic programming",
publisher = "Morgan Kaufmann",
publisher_address = "San Mateo, CA, USA",
month = "2025 " # aug,
abstract = "Existing approaches to artificial intelligence
problems such as sequence induction, automatic
programming, machine learning, planning, and pattern
recognition typically require specification in advance
of the size and shape of the solution to the problem
(often in a unnatural and difficult way). This paper
reports on a new approach in which the size and shape
of the solution to such problems is dynamically created
using Darwinian principles of reproduction and survival
of the fittest. Moreover, the resulting solution is
inherently hierarchical. The paper describes computer
experiments, using the author's 4341 line LISP program,
in five areas of artificial intelligence, namely (1)
sequence induction (e.g. inducing a computational
procedure for the recursive Fibonacci sequence and
inducing a computational procedure for a cubic
polynomial sequence), (2) automatic programming (e.g.
discovering a computational procedure for solving pairs
of linear equations, solving quadratic equations for
complex roots, and discovering trigonometric
identities), (3) machine learning of functions (e.g.
learning a Boolean multiplexer function previously
studied in neural net and classifier system work and
learning the exclusiveor and parity function), (4)
planning (e.g. developing a robotic action sequence
that can stack an arbitrary initial configuration of
blocks into a specified order), and (5) pattern
recognition (e.g. translationinvariant recognition of
a simple one dimensional shape in a linear retina).",
notes = "Held in Detroit, MI, USA?
",
}
