Towards Practical Autoconstructive Evolution: Self-Evolution of Problem-Solving Genetic Programming Systems
Created by W.Langdon from
gp-bibliography.bib Revision:1.8010
- @InCollection{spector:2010:GPTP,
-
author = "Lee Spector",
-
title = "Towards Practical Autoconstructive Evolution:
Self-Evolution of Problem-Solving Genetic Programming
Systems",
-
booktitle = "Genetic Programming Theory and Practice VIII",
-
year = "2010",
-
editor = "Rick Riolo and Trent McConaghy and
Ekaterina Vladislavleva",
-
series = "Genetic and Evolutionary Computation",
-
volume = "8",
-
address = "Ann Arbor, USA",
-
month = "20-22 " # may,
-
publisher = "Springer",
-
chapter = "2",
-
pages = "17--33",
-
keywords = "genetic algorithms, genetic programming, meta-genetic
programming, autoconstructive evolution, Push, PushGP,
Pushpop, AutoPush",
-
isbn13 = "978-1-4419-7746-5",
-
URL = "http://www.springer.com/computer/ai/book/978-1-4419-7746-5",
-
URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.472.6583",
-
URL = "http://faculty.hampshire.edu/lspector/pubs/spector-gptp10-preprint.pdf",
-
DOI = "doi:10.1007/978-1-4419-7747-2_2",
-
size = "18 pages",
-
abstract = "Most genetic programming systems use hard-coded
genetic operators that are applied according to
user-specified parameters. Because it is unlikely that
the provided operators or the default parameters will
be ideal for all problems or all program
representations, practitioners often devote
considerable energy to experimentation with
alternatives. Attempts to bring choices about operators
and parameters under evolutionary control, through
self-adaptative algorithms or meta-genetic programming,
have been explored in the literature and have produced
interesting results. However, no systems based on such
principles have yet been demonstrated to have greater
practical problem-solving power than the more-standard
alternatives. This chapter explores the prospects for
extending the practical power of genetic programming
through the refinement of an approach called
autoconstructive evolution, in which the algorithms
used for the reproduction and variation of evolving
programs are encoded in the programs themselves, and
are thereby subject to variation and evolution in
tandem with their problem-solving components. We
present the motivation for the autoconstructive
evolution approach, show how it can be instantiated
using the Push programming language, summarise previous
results with the Pushpop system, outline the more
recent AutoPush system, and chart a course for future
work focused on the production of practical systems
that can solve hard problems.",
-
notes = "part of \cite{Riolo:2010:GPTP}",
- }
Genetic Programming entries for
Lee Spector
Citations