Program Search with a Hierarchical Variable Length
Representation: Genetic Programming, Simulated
Annealing and Hill Climbing
U. O'Reilly, und F. Oppacher. Parallel Problem Solving from Nature -- PPSN III, 866, Seite 397--406. Jerusalem, Springer-Verlag, (9-14 October 1994)
Zusammenfassung
This paper presents a comparison of Genetic
Programming(GP) with Simulated Annealing (SA) and
Stochastic Iterated Hill Climbing (SIHC) based on a
suite of program discovery problems which have been
previously tackled only with GP. All three search
algorithms employ the hierarchical variable length
representation for programs brought into recent
prominence with the GP paradigm. We feel it is not
intuitively obvious that mutation-based adaptive search
can handle program discovery yet, to date, for each GP
problem we have tried, SA or SIHC also work.
%0 Conference Paper
%1 OReilly:1994:GPSAHC
%A O'Reilly, Una-May
%A Oppacher, Franz
%B Parallel Problem Solving from Nature -- PPSN III
%C Jerusalem
%D 1994
%E Davidor, Yuval
%E Schwefel, Hans-Paul
%E Manner, Reinhard
%I Springer-Verlag
%K algorithms, genetic programming
%N 866
%P 397--406
%T Program Search with a Hierarchical Variable Length
Representation: Genetic Programming, Simulated
Annealing and Hill Climbing
%U http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-58484-6
%X This paper presents a comparison of Genetic
Programming(GP) with Simulated Annealing (SA) and
Stochastic Iterated Hill Climbing (SIHC) based on a
suite of program discovery problems which have been
previously tackled only with GP. All three search
algorithms employ the hierarchical variable length
representation for programs brought into recent
prominence with the GP paradigm. We feel it is not
intuitively obvious that mutation-based adaptive search
can handle program discovery yet, to date, for each GP
problem we have tried, SA or SIHC also work.
%@ 3-540-58484-6
@inproceedings{OReilly:1994:GPSAHC,
abstract = {This paper presents a comparison of Genetic
Programming(GP) with Simulated Annealing (SA) and
Stochastic Iterated Hill Climbing (SIHC) based on a
suite of program discovery problems which have been
previously tackled only with GP. All three search
algorithms employ the hierarchical variable length
representation for programs brought into recent
prominence with the GP paradigm. We feel it is not
intuitively obvious that mutation-based adaptive search
can handle program discovery yet, to date, for each GP
problem we have tried, SA or SIHC also work.},
added-at = {2008-06-19T17:46:40.000+0200},
address = {Jerusalem},
author = {O'Reilly, Una-May and Oppacher, Franz},
biburl = {https://www.bibsonomy.org/bibtex/234020df7a78259bf16a8c263c051a116/brazovayeye},
booktitle = {Parallel Problem Solving from Nature -- PPSN III},
editor = {Davidor, Yuval and Schwefel, Hans-Paul and Manner, Reinhard},
interhash = {a650173505c49823fe235cf11272c952},
intrahash = {34020df7a78259bf16a8c263c051a116},
isbn = {3-540-58484-6},
keywords = {algorithms, genetic programming},
month = {9-14 October},
notes = {PPSN3
A longer version is SFI Technical Report 94-04-021.},
number = 866,
pages = {397--406},
publisher = {Springer-Verlag},
publisher_address = {Berlin, Germany},
series = {Lecture Notes in Computer Science},
size = {10 pages},
timestamp = {2008-06-19T17:49:03.000+0200},
title = {Program Search with a Hierarchical Variable Length
Representation: Genetic Programming, Simulated
Annealing and Hill Climbing},
url = {http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-58484-6},
year = 1994
}