Results 1 - 10 of 68

Integrating User Preferences and Decomposition Methods for Many-objective Optimization

by Asad Mohammadi, Mohammad Nabi Omidvar, Xiaodong Li, Kalyanmoy Deb
"... Abstract—Evolutionary algorithms that rely on dominance ranking often suffer from a low selection pressure problem when dealing with many-objective problems. Decomposition and user-preference based methods can help to alleviate this problem to a great extent. In this paper, a user-preference based e ..."
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Abstract—Evolutionary algorithms that rely on dominance ranking often suffer from a low selection pressure problem when dealing with many-objective problems. Decomposition and user-preference based methods can help to alleviate this problem to a great extent. In this paper, a user-preference based

IEEE CEC Reference Point Based Multi-objective Optimization Through Decomposition

by Asad Mohammadi, Mohammad Nabi Omidvar, Xiaodong Li
"... Abstract—In this paper we propose a user-preference based evolutionary algorithm that relies on decomposition strategies to convert a multi-objective problem into a set of single-objective problems. The use of a reference point allows the algorithm to focus the search on more preferred regions which ..."
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Abstract—In this paper we propose a user-preference based evolutionary algorithm that relies on decomposition strategies to convert a multi-objective problem into a set of single-objective problems. The use of a reference point allows the algorithm to focus the search on more preferred regions

Using Planning for Query Decomposition in Bioinformatics

by unknown authors
"... Domains like bioinformatics are complex data integration domains because data from remote sources and specialized applications need to be combined to answer queries. An important characteristic of such a domain is that actions may be mutually exclusive or causally related. Moreover, there is (partia ..."
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that can incorporate partial query decomposition, source preferences, data and application interaction, and temporal constraints.

1 Smoothing and Decomposition for Analysis Sparse Recovery

by Zhao Tan, Student Member, Yonina C. Eldar, Amir Beck, Arye Nehorai
"... ar ..."
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Abstract not found

Object/relational query optimization with chase

by Lucian Popa
"... and backchase ..."
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and backchase

Evolutionary Algorithms Based on Decomposition and Indicator Functions: State-of-the-art Survey

by Wali Khan Mashwani, Abdellah Salhi, Muhammad Asif Jan, Muhammad Sulaiman, Rashida Adeeb Khanum, Abdulmohsen Algarni
"... Abstract—In the last two decades, multiobjective optimization has become mainstream because of its wide applicability in a variety of areas such engineering, management, the military and other fields. Multi-Objective Evolutionary Algorithms (MOEAs) play a dominant role in solving problems with multi ..."
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efficient techniques including those which use preference based information, local search optimizers, multiple ensemble search operators together with self-adaptive strategies, metaheuristics, mating restriction approaches, statistical sampling techniques, integration of Fuzzy dominance concepts and many

Fast nonparametric matrix factorization for large-scale collaborative filtering. The 32nd SIGIR conference

by Kai Yu, Shenghuo Zhu, John Lafferty, Yihong Gong , 2009
"... With the sheer growth of online user data, it becomes challenging to develop preference learning algorithms that are sufficiently flexible in modeling but also affordable in computation. In this paper we develop nonparametric matrix factorization methods by allowing the latent factors of two low-ran ..."
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With the sheer growth of online user data, it becomes challenging to develop preference learning algorithms that are sufficiently flexible in modeling but also affordable in computation. In this paper we develop nonparametric matrix factorization methods by allowing the latent factors of two low

Value Decomposition and Dimension Selection in Multi-Dimensional Datasets using Map-Reduce Operation

by Preethi V, Suriya M
"... The datasets which are in the form of object-attribute-time format is referred to as three-dimensional (3D) data sets. Clustering these three-dimensional (3D) data sets is a difficult task. So the subspace clustering method is applied to cluster the three-dimensional (3D) data sets. But finding the ..."
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centroid based concept is introduced called CATS. This CATS allows the users to select the preferred objects as centroids. This algorithm is not the parallel one. So it increases the time and space requirements which are needed to cluster the three-dimensional (3D) data sets. And in CATS no optimal

CERTIFICATE OF APPROVAL

by Yunlong Liu, Yunlong Liu, Erwei Bai, Mona K. Garvin, Hantao Zhang , 2014
"... Treatment plan optimization for rotating-shield brachytherapy ..."
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Treatment plan optimization for rotating-shield brachytherapy

Optimal Privacy-aware Path in Hippocratic Databases?

by Min Li, Xiaoxun Sun, Hua Wang, Yanchun Zhang
"... Abstract. Privacy becomes a major concern for both customers and en-terprises in today’s corporate marketing strategies, many research efforts have been put into developing new privacy-aware technologies. Among them, Hippocratic databases are one of the important mechanisms to guarantee the respect ..."
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processes in Hippocratic databases. A compre-hensive approach for negotiation of personal information between cus-tomers and enterprises based on user preferences is developed when enterprises offer their clients a number of ways to fulfill a service. We organize purposes into purpose directed graphs
Results 1 - 10 of 68