Zobrazeno 1 - 10
of 81
pro vyhledávání: '"Ashraf M. Abdelbar"'
Autor:
Ashraf M. Abdelbar, Khalid M. Salama
Publikováno v:
IEEE Access, Vol 7, Pp 18464-18479 (2019)
ACOR is a well-established ant colony optimization algorithm for continuous-domain optimization. We present an approach for the dynamic adaptation of the ACOR algorithm's controlling parameters, focusing on the search width parameter, based on using
Externí odkaz:
https://doaj.org/article/7aa191be8d964188ab2e37dbff235b0d
Autor:
Ashraf M. Abdelbar
Publikováno v:
Algorithms, Vol 5, Iss 4, Pp 521-528 (2012)
The minimax algorithm, also called the negamax algorithm, remains today the most widely used search technique for two-player perfect-information games. However, minimaxing has been shown to be susceptible to game tree pathology, a paradoxical situati
Externí odkaz:
https://doaj.org/article/bcf552c95831489caaca327ad2ad5cca
Autor:
Ashraf M. Abdelbar, Thomas Humphries, Jesús Guillermo Falcón-Cardona, Carlos A. Coello Coello
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031201752
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6eaee65662729ee13f08fbce5473830c
https://doi.org/10.1007/978-3-031-20176-9_22
https://doi.org/10.1007/978-3-031-20176-9_22
Publikováno v:
Intelligent Data Analysis. 21:913-944
Instance-based learning (IBL) methods predict the class label of a new instance based directly on the distance between the new unlabeled instance and each labeled instance in the training set, without constructing a classification model in the traini
Autor:
Ashraf M. Abdelbar, Khalid M. Salama
Publikováno v:
Swarm Intelligence. 11:211-242
Classification is a data mining task the goal of which is to learn a model, from a training dataset, that can predict the class of a new data instance, while clustering aims to discover natural instance-groupings within a given dataset. Learning clus
Autor:
Carlos A. Coello Coello, Ashraf M. Abdelbar, Jesús Guillermo Falcón-Cardona, Khalid M. Salama
Publikováno v:
SSCI
Commonly, Ant Colony Optimization algorithms have been applied to the solution of single-and multi-objective optimization problems (MOPs). However, in recent years, a number of approaches have been proposed to solve problems with continuous search sp
Publikováno v:
Intelligent Data Analysis. 20:1021-1059
Publikováno v:
IJCNN
One of the leading approaches to collaborative filtering is to use matrix factorization to discover a set of latent factors that explain the pattern of preferences. In this paper, we apply a resilient stochastic gradient descent approach that uses on
Autor:
Khalid M. Salama, Ashraf M. Abdelbar
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030005320
ANTS Conference
ANTS Conference
The ACO\(\mathbb {_R}\) algorithm is based on the Ant Colony Optimization (ACO) metaphor, and a crossover operator does not naturally within this metaphor. In spite of this, we investigate in this paper whether the performance of ACO\(\mathbb {_R}\)
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ba471190e7c6aeb5d4876b000d67ca83
https://doi.org/10.1007/978-3-030-00533-7_28
https://doi.org/10.1007/978-3-030-00533-7_28
Autor:
Ashraf M. Abdelbar, Khalid M. Salama
Publikováno v:
SSCI
In this paper, we present a recombination-based extension of the recently-introduced iMOACO R algorithm. iMOACO R itself is based on ACO R , an Ant Colony Optimization (ACO) optimizer for continuous search spaces, and extends ACO R to Multi-Objective