Zobrazeno 1 - 10
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pro vyhledávání: '"Jhon Edgar Amaya"'
Publikováno v:
Memetic Computing. 12:3-22
Memetic algorithms are techniques that orchestrate the interplay between population-based and trajectory-based algorithmic components. In particular, some memetic models can be regarded under this broad interpretation as a group of autonomous basic o
Publikováno v:
International Journal of Computational Intelligence Systems, Vol 6, Iss 3 (2013)
Externí odkaz:
https://doaj.org/article/29bc706c8ba44fbbb5740445d7864bd0
Publikováno v:
International Journal of Computational Intelligence Systems, Vol 6, Iss 3 (2013)
This paper presents a parameterized schema for building memetic algorithms based on cross-entropy (CE) methods. This novel schema is general in nature, and features multiple probability mass functions and Lamarckian learning. The applicability of the
Externí odkaz:
https://doaj.org/article/203b8563e5d64400a57f02e0df7e11ba
Publikováno v:
Computers & Industrial Engineering. 162:107764
The paper presents a proposal about the Cooperative Cross-Entropy to solve Combinatorial Optimization Problems. The cooperative scheme consists of the definition of several instances of the Cross-Entropy method and the establishment of an exchange po
Publikováno v:
SCCC
The research focused on optimizing parameters of a PID controller using metaheuristics, aimed at improving the response of PID controls, especially those where processes manifest a pronounced nonlinear behavior and for which traditional tuning method
Publikováno v:
Artificial Intelligence for Engineering Design, Analysis and Manufacturing. 26:221-235
The tool switching problem (ToSP) is well known in the domain of flexible manufacturing systems. Given a reconfigurable machine, the ToSP amounts to scheduling a collection of jobs on this machine (each of them requiring a different set of tools to b
Publikováno v:
Memetic Computing. 3:199-216
This work deals with memetic-computing agent-models based on the cooperative integration of search agents endowed with (possibly different) optimization strategies, in particular memetic algorithms. As a proof-of-concept of the model, we deploy it on
Publikováno v:
Springer Handbook of Computational Intelligence ISBN: 9783662435045
Handbook of Computational Intelligence
Handbook of Computational Intelligence
This chapter presents an overview of hybridization mechanisms in evolutionary algorithms. Such mechanisms are aimed to introducing problem knowledge in the optimization technique by means of the synergistic combination of general–purpose methods an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0cc7566b74bc33f9259f0f232f0f8038
https://doi.org/10.1007/978-3-662-43505-2_52
https://doi.org/10.1007/978-3-662-43505-2_52
Publikováno v:
International Journal of Computational Intelligence Systems, Vol 6, Iss 3 (2013)
International Journal of Computational Intelligence Systems, Vol 6, Iss 3 (2017)
International Journal of Computational Intelligence Systems, Vol 6, Iss 3 (2017)
This paper presents a parameterized schema for building memetic algorithms based on cross-entropy (CE) methods. This novel schema is general in nature, and features multiple probability mass functions and Lamarckian learning. The applicability of the
Autor:
Luis A. Mora, Jhon Edgar Amaya
Publikováno v:
CLEI
This paper presents a new methodology for the development of multi-classifiers SVM with One-Againts-One (OAO), which allows each node to use different features or attributes to differentiate each pair of classes, called asymmetric OAO-SVM. We evaluat