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Author (up) Allende-Cid, H.; Canessa, E.; Quezada, A.; Allende, H.
Title An Improved Fuzzy Rule-Based Automated Trading Agent Type
Year 2011 Publication Studies In Informatics And Control Abbreviated Journal Stud. Inform. Control
Volume 20 Issue 2 Pages 135-142
Keywords Automated Trading Agents; Fuzzy Rule-based Agents
Abstract In this paper an improved Fuzzy Rule-Based Trading Agent is presented. The proposal consists in adding machine-learning-based methods to improve the overall performance of an automated agent that trades in futures markets. The modified Fuzzy Rule-Based Trading Agent has to decide whether to buy or sell goods, based on the spot and futures time series, gaining a profit from the price speculation. The proposal consists first in changing the membership functions of the fuzzy inference model (Gaussian and Sigmoidal, instead of triangular and trapezoidal). Then using the NFAR (Neuro-Fuzzy Autoregressive) model the relevant lags of the time series are detected, and finally a fuzzy inference system (Self-Organizing Neuro-Fuzzy Inference System) is implemented to aid the decision making process of the agent. Experimental results demonstrate that with the addition of these techniques, the improved agent considerably outperforms the original one.
Address [Allende-Cid, H; Allende, H] Univ Tecn Federico Santa Maria, Dept Informat, Valparaiso 2390123, Chile, Email: vector@inf.utfsm.cl
Corporate Author Thesis
Publisher Natl Inst R&D Informatics-Ici Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1220-1766 ISBN Medium
Area Expedition Conference
Notes WOS:000292015600006 Approved
Call Number UAI @ eduardo.moreno @ Serial 157
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Author (up) Canessa, E.; Chaigneau, S.
Title Calibrating Agent-Based Models Using a Genetic Algorithm Type
Year 2015 Publication Studies In Informatics And Control Abbreviated Journal Stud. Inform. Control
Volume 24 Issue 1 Pages 79-90
Keywords Agent-based modelling; genetic algorithms; calibration; validation; relational equivalence; complex adaptive systems
Abstract We present a Genetic Algorithm (GA)-based tool that calibrates Agent-based Models (ABMs). The GA searches through a user-defined set of input parameters of an ABM, delivering values for those parameters so that the output time series of an ABM may match the real system's time series to certain precision. Once that set of possible values has been available, then a domain expert can select among them, the ones that better make sense from a practical point of view and match the explanation of the phenomenon under study. In developing the GA, we have had three main goals in mind. First, the GA should be easily used by non-expert computer users and allow the seamless integration of the GA with different ABMs. Secondly, the GA should achieve a relatively short convergence time, so that it may be practical to apply it to many situations, even if the corresponding ABMs exhibit complex dynamics. Thirdly, the GA should use a few data points of the real system's time series and even so, achieve a sufficiently good match with the ABM's time series to attaining relational equivalence between the real system under study and the ABM that models it. That feature is important since social science longitudinal studies commonly use few data points. The results show that all of those goals have been accomplished.
Address [Canessa, Enrique] Univ Adolfo Ibanez, CINCO, Fac Ingn & Ciencias, Vina Del Mar, Chile, Email: ecanessa@uai.cl;
Corporate Author Thesis
Publisher Natl Inst R&D Informatics-Ici Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1220-1766 ISBN Medium
Area Expedition Conference
Notes WOS:000351892900009 Approved
Call Number UAI @ eduardo.moreno @ Serial 481
Permanent link to this record