In this paper a new evolutionary paradigm, called
Multi-Expression Programming (MEP), intended for
solving computationally difficult problems is proposed.
A new encoding method is designed. MEP individuals are
linear entities that encode complex computer programs.
In this paper MEP is used for solving some
computationally difficult problems like symbolic
regression, game strategy discovering, and for
generating heuristics. Other exciting applications of
MEP are suggested. Some of them are currently under
development. MEP is compared with Gene Expression
Programming (GEP) by using a well-known test problem.
For the considered problems MEP performs better than
GEP.
%0 Generic
%1 oltean:2002:MEP
%A Oltean, Mihai
%A Dumitrescu, D.
%D 2002
%K Computation, Evolutionary Expression Multi Programming, Tic-Tac-Toe, algorithms, game generation. genetic heuristics linear programming, regression, representation, strategy, symbolic
%T Multi Expression Programming
%U http://www.mep.cs.ubbcluj.ro/oltean_pdf.pdf
%X In this paper a new evolutionary paradigm, called
Multi-Expression Programming (MEP), intended for
solving computationally difficult problems is proposed.
A new encoding method is designed. MEP individuals are
linear entities that encode complex computer programs.
In this paper MEP is used for solving some
computationally difficult problems like symbolic
regression, game strategy discovering, and for
generating heuristics. Other exciting applications of
MEP are suggested. Some of them are currently under
development. MEP is compared with Gene Expression
Programming (GEP) by using a well-known test problem.
For the considered problems MEP performs better than
GEP.
@misc{oltean:2002:MEP,
abstract = {In this paper a new evolutionary paradigm, called
Multi-Expression Programming (MEP), intended for
solving computationally difficult problems is proposed.
A new encoding method is designed. MEP individuals are
linear entities that encode complex computer programs.
In this paper MEP is used for solving some
computationally difficult problems like symbolic
regression, game strategy discovering, and for
generating heuristics. Other exciting applications of
MEP are suggested. Some of them are currently under
development. MEP is compared with Gene Expression
Programming (GEP) by using a well-known test problem.
For the considered problems MEP performs better than
GEP.},
added-at = {2008-06-19T17:46:40.000+0200},
author = {Oltean, Mihai and Dumitrescu, D.},
biburl = {https://www.bibsonomy.org/bibtex/246c32ff625d1492c2452da84dc370c04/brazovayeye},
email = {ddumitr@nessie.cs.ubbcluj.ro},
interhash = {1cf5f95d9fd8b930eff16842ffd05998},
intrahash = {46c32ff625d1492c2452da84dc370c04},
keywords = {Computation, Evolutionary Expression Multi Programming, Tic-Tac-Toe, algorithms, game generation. genetic heuristics linear programming, regression, representation, strategy, symbolic},
month = May,
note = {submitted},
notes = {Note critisism on GP-list of {"}MEP better than GEP{"}
cf. \cite{ferreira:2001:CS} 15 May 2002
Oct 2006 oltean_pdf.pdf seems to be a newer version.},
size = {33 pages},
timestamp = {2008-06-19T17:48:48.000+0200},
title = {Multi Expression Programming},
url = {http://www.mep.cs.ubbcluj.ro/oltean_pdf.pdf},
year = 2002
}