TY - GEN
T1 - Presenting the ECO
T2 - 20th European Conference on the Applications of Evolutionary Computation, EvoApplications 2017
AU - Yaman, A.
AU - Hallawa, A.
AU - Coler, M.
AU - Iacca, G.
PY - 2017
Y1 - 2017
N2 - © Springer International Publishing AG 2017.A well-established notion in Evolutionary Computation (EC) is the importance of the balance between exploration and exploitation. Data structures (e.g. for solution encoding), evolutionary operators, selection and fitness evaluation facilitate this balance. Furthermore, the ability of an Evolutionary Algorithm (EA) to provide efficient solutions typically depends on the specific type of problem. In order to obtain the most efficient search, it is often needed to incorporate any available knowledge (both at algorithmic and domain level) into the EA. In this work, we develop an ontology to formally represent knowledge in EAs. Our approach makes use of knowledge in the EC literature, and can be used for suggesting efficient strategies for solving problems by means of EC.We call our ontology “Evolutionary Computation Ontology” (ECO). In this contribution, we show one possible use of it, i.e. to establish a link between algorithm settings and problem types. We also show that the ECO can be used as an alternative to the available parameter selection methods and as a supporting tool for algorithmic design.
AB - © Springer International Publishing AG 2017.A well-established notion in Evolutionary Computation (EC) is the importance of the balance between exploration and exploitation. Data structures (e.g. for solution encoding), evolutionary operators, selection and fitness evaluation facilitate this balance. Furthermore, the ability of an Evolutionary Algorithm (EA) to provide efficient solutions typically depends on the specific type of problem. In order to obtain the most efficient search, it is often needed to incorporate any available knowledge (both at algorithmic and domain level) into the EA. In this work, we develop an ontology to formally represent knowledge in EAs. Our approach makes use of knowledge in the EC literature, and can be used for suggesting efficient strategies for solving problems by means of EC.We call our ontology “Evolutionary Computation Ontology” (ECO). In this contribution, we show one possible use of it, i.e. to establish a link between algorithm settings and problem types. We also show that the ECO can be used as an alternative to the available parameter selection methods and as a supporting tool for algorithmic design.
UR - https://www.scopus.com/pages/publications/85017531487
UR - https://www.scopus.com/pages/publications/85017531487#tab=citedBy
U2 - 10.1007/978-3-319-55849-3_39
DO - 10.1007/978-3-319-55849-3_39
M3 - Conference contribution
SN - 9783319558486
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 603
EP - 619
BT - Applications of Evolutionary Computation - 20th European Conference, EvoApplications 2017, Proceedings
A2 - Hidalgo, J.I.
A2 - Cotta, C.
A2 - Hu, T.
A2 - Tonda, A.
A2 - Burrelli, P.
A2 - Coler, M.
A2 - Iacca, G.
A2 - Kampouridis, M.
A2 - Mora Garcia, A.M.
A2 - Squillero, G.
A2 - Brabazon, A.
A2 - Haasdijk, E.
A2 - Heinerman, J.
A2 - D Andreagiovanni, F.
A2 - Bacardit, J.
A2 - Nguyen, T.T.
A2 - Silva, S.
A2 - Tarantino, E.
A2 - Esparcia-Alcazar, A.I.
A2 - Ascheid, G.
A2 - Glette, K.
A2 - Cagnoni, S.
A2 - Kaufmann, P.
A2 - de Vega, F.F.
A2 - Mavrovouniotis, M.
A2 - Zhang, M.
A2 - Divina, F.
A2 - Sim, K.
A2 - Urquhart, N.
A2 - Schaefer, R.
PB - Springer Verlag
Y2 - 19 April 2017 through 21 April 2017
ER -