• A comparison between Geometric...

Result: A comparison between Geometric Semantic GP and Cartesian GP for Boolean functions learning?

Title:
A comparison between Geometric Semantic GP and Cartesian GP for Boolean functions learning?
Authors:
Mambrini, A, Manzoni, L, MANZONI, LUCA
Publisher Information:
Association for Computing Machinery 2014
Document Type:
Electronic Resource Electronic Resource
Index Terms:
Boolean function, Cartesian genetic programing, Geometric Semantic Genetic Programming, Computational Theory and Mathematic, Applied Mathematics, info:eu-repo/semantics/conferenceObject
URL:
http://hdl.handle.net/10281/60838
Availability:
Open access content. Open access content
Note:
English
Other Numbers:
ITBAO oai:boa.unimib.it:10281/60838
10.1145/2598394.2598475
info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-84905644410
1311390412
Contributing Source:
BICOCCA OPEN ARCH
From OAIster®, provided by the OCLC Cooperative.
Accession Number:
edsoai.on1311390412
Database:
OAIster

Further Information

Geometric Semantic Genetic Programming (GSGP) is a recently defined form of Genetic Programming (GP) that has shown promising results on single output Boolean problems when compared with standard tree-based GP. In this paper we compare GSGP with Cartesian GP (CGP) on comprehensive set of Boolean benchmarks, consisting of both single and multiple outputs Boolean problems. The results obtained show that GSGP outperforms also CGP, confirming the efficacy of GSGP in solving Boolean problems.