Genetic Programming with Mixed Integer Linear Programming Based Library Search
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{Huynh:ieeeTEC,
-
author = "Quang Nhat Huynh and Shelvin Chand and
Hemant Kumar Singh and Tapabrata Ray",
-
title = "Genetic Programming with Mixed Integer Linear
Programming Based Library Search",
-
journal = "IEEE Transactions on Evolutionary Computation",
-
year = "2018",
-
volume = "22",
-
number = "5",
-
pages = "733--747",
-
month = oct,
-
keywords = "genetic algorithms, genetic programming, Mixed Integer
Linear Programming, MLIP, Semantic Backpropagation, SB,
Library of Sub-expressions",
-
ISSN = "1089-778X",
-
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8364611",
-
DOI = "doi:10.1109/TEVC.2018.2840056",
-
size = "15 pages",
-
abstract = "Genetic programming (GP) is one of the commonly used
tools for symbolic regression. In the field of GP, the
use of semantics and an external library of
sub-expressions for designing better search operators
has recently gained significant attention. A notable
example is semantic back-propagation, which has
demonstrated an ability to obtain expressions with
extremely small prediction errors. However, these
expressions often tend to be long and difficult to
interpret, which may restrict their applicability in
real-life problems. In this paper, we propose a GP
framework that includes two key elements, a new library
construction scheme and a novel semantic operator based
on mixed-integer linear programming (MILP). The
proposed library construction scheme maintains diverse
sub-expressions and keeps the library size in check by
imposing an upper limit. The proposed semantic operator
constructs new expressions by effectively combining a
given number of sub-expressions from the library. These
improvements have been integrated in a bi-objective GP
framework with random desired operator (RDO), which
attempts to simultaneously reduce the complexity and
improve the fitness of the evolving expressions. The
contributions of individual components are studied in
detail using fifteen benchmarks. It is observed that
the use of the proposed scheme with RDO leads to
shorter expressions without sacrificing accuracy of
approximation. The addition of MILP further improves
the results for certain types of problems.",
-
notes = "also known as \cite{8364611}",
- }
Genetic Programming entries for
Quang Nhat Huynh
Shelvin Chand
Hemant Kumar Singh
Tapabrata Ray
Citations