Guidelines for defining benchmark problems in Genetic Programming

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Title: Guidelines for defining benchmark problems in Genetic Programming
Authors: Nicolau, Miguel
Agapitos, Alexandros
O'Neill, Michael
Brabazon, Anthony
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Date: 28-May-2015
Online since: 2017-01-04T12:52:33Z
Abstract: The field of Genetic Programming has recently seen a surge of attention to the fact that benchmarking and comparison of approaches is often done in non-standard ways, using poorly designed comparison problems. We raise some issues concerning the design of benchmarks, within the domain of symbolic regression, through experimental evidence. A set of guidelines is provided, aiming towards careful definition and use of artificial functions as symbolic regression benchmarks.
Type of material: Conference Publication
Publisher: IEEE
Copyright (published version): 2015 IEEE
Keywords: Genetic programmingBenchmarksSymbolic regressionRegressionModel building
DOI: 10.1109/CEC.2015.7257019
Language: en
Status of Item: Peer reviewed
Conference Details: IEEE Congress on Evolutionary Computation, CEC 2015, Sendai, Japan, May 25-28, 2015, Proceedings, Sendai, Japan, May, 2015
Appears in Collections:Business Research Collection
CASL Research Collection

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