Treffer: Inverse design of cellular automata by genetic algorithms : An unconventional programming paradigm

Title:
Inverse design of cellular automata by genetic algorithms : An unconventional programming paradigm
Source:
UPP 2004 : unconventional programming paradigms (15-17 September 2004, Mont Saint Michel, revised selected & invited papers)Lecture notes in computer science. :161-172
Publisher Information:
Berlin: Springer, 2005.
Publication Year:
2005
Physical Description:
print, 16 ref
Original Material:
INIST-CNRS
Document Type:
Konferenz Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Universiteit Leiden, LIACS, P.O. Box 9512, 2300 RA Leiden, Netherlands
NuTech Solutions GmbH, Martin Schmeisser Weg 15, 44227 Dortmund, Germany
ISSN:
0302-9743
Rights:
Copyright 2005 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.17134470
Database:
PASCAL Archive

Weitere Informationen

Evolving solutions rather than computing them certainly represents an unconventional programming approach. The general methodology of evolutionary computation has already been known in computer science since more than 40 years, but their utilization to program other algorithms is a more recent invention. In this paper, we outline the approach by giving an example where evolutionary algorithms serve to program cellular automata by designing rules for their iteration. Three different goals of the cellular automata designed by the evolutionary algorithm are outlined, and the evolutionary algorithm indeed discovers rules for the CA which solve these problems efficiently.