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43rd Annual Association for Computing Machinery Southeast Conference, ACMSE '05

作者： Ketel, Mohammed Homaifar, Abdullah Department of Computer Science North Carolina AandT State University Greensboro NC 27411 United States NASA Autonomous Control and Info. Tech. Center North Carolina AandT State University Greensboro NC 27411 United States

ISBN: (纸本)1595930590

Many data mining applications deal with large data sets that contain private information that must be protected. This has led to the development of many privacy-preserving data mining techniques. Many of these techniques use randomized data distortion by adding noise to the sensitive data. However, noncareful noise addition may introduce biases to the statistical parameters of these data, including means and variances. To meet privacy requirements and preserve the statistical properties of the sensitive data we use a data transformation technique called Rotation-Based Transformation (RBT). This method distorts only confidential numerical attributes and preserves the statistical properties of the data. Copyright 2005 ACM.

关键词： Data mining

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Distributed sensor networks based on mobile agents paradigm

Distributed sensor networks based on mobile agents paradigm

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Southeastern Symposium on System Theory

作者： M. Ketel N.S. Dogan A. Homaifar Department of Computer Science North Carolina Agriculture and Technical State University Greensboro NC USA Department of Electrical & Computer Engineering North Carolina Agriculture and Technical State University Greensboro NC USA NASA Autonomous Control and Information Technology Center North Carolina Agriculture and Technical State University Greensboro NC USA

Distributed sensor networks (DSNs) are being developed for a wide range of applications. A sensor networks consist of a large number of nodes performing distributed sensing/event detection. Sensor nodes are energy-constrained, efficient routing is essential for increasing the lifetime of a sensor network. A DSN requires interoperability, low latency, and low power consumption in order to operate for long periods of time. Due to power constraints, DSN should be able to operate at low data rates and still delivering acceptable quality of service. In a DSN the energy required for communications tasks is usually much greater than the energy required for computational tasks. To reduce the data communication between nodes and to maximize the network lifetime, we propose to use the mobile-agent paradigm. In mobile-agent based DSN9 instead of moving data from an individual sensor node to a processing center as in the client-server based computing, mobile agents are dispatched from the processing center to the sensor nodes and process data locally. By moving code to the data, a mobile agent can reduce latency, bandwidth and vulnerability to network disconnection.

关键词： Mobile agents computer networks Routing Data communication Bandwidth Wireless communication Application software Delay Wireless sensor networks Capacitive sensors

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Genetic algorithms solution for unconstrained optimal crane control

Genetic algorithms solution for unconstrained optimal crane ...

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1999 Congress on Evolutionary Computation, CEC 1999

作者： Kimiaghalam, Bahram Homaifar, Abdollah Bikdash, Marwan Dozier, Gerry NASA-Autonomous Control Engineering Center Department of Electrical Engineering NC AandT State University Greensboro NC United States Department of Computer Science and Engineering Auburn University AL 36849-5347 United States

ISBN: (纸本)0780355369

Crane control is a difficult problem for conventional control methods because of the highly nonlinear equations that must be satisfied. Usually the necessary conditions for solving an optimal control problem require finding the initial co-state vector. In this paper real-coded genetic algorithms are used to find the desired initial value of the costates of the system with no constraints. In our genetic representation, each chromosome represents a set of co-states and each gene (co-state) has an associated cost based on its ability to move the system to desired state after a given amount of time. The objective is to evolve a minimum cost co-state. Our results for this unconstrained crane problem are quite encouraging. © 1999 IEEE.

关键词： Genetic algorithms

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Genetic algorithms solution for unconstrained optimal crane control

Genetic algorithms solution for unconstrained optimal crane ...

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Congress on Evolutionary Computation

作者： B. Kimiaghalam A. Homaifar M. Bikdash G. Dozier NASA Autonomous Control Engineering Center Greensboro NC USA Department of Computer Science and Engineering Aubum University AL USA

关键词： Genetic algorithms Cranes Optimal control Boundary value problems control engineering Motion control Biological cells Costs Equations Gears

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Multiobjective evolutionary path planning via fuzzy tournament selection

Multiobjective evolutionary path planning via fuzzy tourname...

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IEEE International Conference on Evolutionary Computation

作者： G. Dozier S. McCullough A. Homaifar E. Tunstel L. Moore Department of Computer Science & Engineering Aubum University Auburn AL USA NASA Center for Autonomous Control Engineering North Carolina Agriculture and Technical State University Greensboro NC USA Jet Propulsion Laboratory Robotic Vehicles Group Pasadena CA USA

The paper introduces a new selection algorithm that can be used for evolutionary path planning systems. This new selection algorithm combines fuzzy inference along with tournament selection to select candidate paths (CPs) to be parents based on: (1) the Euclidean distance from origin to destination, (2) the sum of the changes in the slope of a path, and (3) the average change in the slope of a path. The authors provide a detailed description of the fuzzy inference system used in the new fuzzy tournament selection algorithm (FTSA) as well as some examples of its usefulness. They use 12 instances of the FTSA to rank a population of CPs using the above criteria. Based on its path ranking capability, they show how the FTSA can obviate the need for the development of an explicit multiobjective evaluation function. Finally, they use the FTSA to enhance the performance of an existing evolutionary path planning system called GEPOA.

关键词： Path planning Inference algorithms Fuzzy systems Euclidean distance Robots computer science nasa Automotive engineering Equations Propulsion

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Fuzzzy constraint network topology and evolutionary hill-climbing 35

Fuzzzy constraint network topology and evolutionary hill-cli...

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35th ACM Annual Southeast Regional Conference, ACM-SE 1997

作者： Dozier, Gerry Homaifar, Abdollah Bowenf, James Esterline, Albert NASA Center for Autonomous Control Engineering North Carolina a and T State University GreensboroNC27411 United States Department of Computer Science National University of Ireland at Cork Cork Ireland

ISBN: (纸本)0897919254

Tematic hybrid which combines the concept of evolutionary hill-climbing search with the systematic search concept of arc revision to form a hybrid that quickly find solutions to Fuzzy Constraint Satisfaction Problems (FCSPs). The performance of this hybrid on 250 randomly generated FCSPs in which th e fuzzy co n stra in ts are evenly d is trib u ted amongst th e variables of th e FCSP is compared with its p e rformance on 250 randomly generated FCSPs where the fuzzy constraints are unevenly dis trib u ted . The results provide some interes ting insights in th e role th a t Fuzzy C ons tra in t Network Topology has on Evolutionary Search. copy;1997 ACM 0-89791-925-4.

关键词： Network topology

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Solving static and dynamic fuzzy constraint networks using evolutionary hill-climbing 6th

Solving static and dynamic fuzzy constraint networks using e...

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6th International Conference on Evolutionary Programming, EP 1997

作者： Dozier, Gerry Bowen, James Homaifar, Abdollah Esterline, Albert NASA Center for Autonomous Control Engineering North Carolina A and T State University GreensboroNC27411 United States Department of Computer Science National University of Ireland at Cork Cork Ireland

ISBN: (纸本)9783540627883

In this paper we combine the concept of evolutionary hillclimbing search with the systematic search concept of arc revision to form a hybrid system that quickly finds solutions to static and dynamic Fuzzy Constraint Truth Optimization Problems (FCTOPs). We compare the static and dynamic methods for solving FCTOPs using a test suite of an additional 250 randomly generated FCTOPs. In the static method, all the constraints of a FCTOP are known by the hybrid system at runtime. In the dynamic method, only half of the constraints of a FCTOP are known at run-time. Each time our hybrid system discovers a solution that satisfies all of the constraints of the the current network, one additional constraint is added. Results show that our algorithm performs exceptionally well in the presence of dynamic (incremental) constraints. © Springer-Verlag Berlin Heidelberg 1997.

关键词： Hybrid systems

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Solving Randomly Generated Static and Dynamic Fuzzy Constraint Networks using Microevolutionary Hill-Climbing

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Intelligent Automation and Soft Computing 1997年第1期3卷 51-62页

作者： Dozier, Gerry Homaifar, Abdollah Esterline, Albert Bowen, James NASA Center for Autonomous Control Engineering North Carolina A&T State University Greensboro NC 27411 United States Department of Computer Science National University of Ireland at Cork Cork Ireland

In this article we combine the concept of microevolutionary hill-climbing search with the systematic search concept of arc revision to form a hybrid system that quickly finds solutions to static and dynamic Fuzzy Constraint Truth Optimization Problems (FCTOPs). Furthermore, we present the results of two experiments. In the first experiment, our microevolutionary hybrid system outperforms a modified version of a well known hill-climber, the Iterative Descent Method (IDM), on a test suite of 500 randomly generated static Fuzzy Constraint Networks. In the second experiment, we compare two methods for solving FCTOPs using a test suite of an additional 250 randomly generated FCTOPs. In the first method, all the constraints of a FCTOP are known by the hybrid system at run-time. We refer to this method as the static method for solving FCTOPs. In the second method, only half of the constraints of a FCTOP are known at run-time. Each time our hybrid system discovers a solution that satisfies all of the constraints of the current network, one additional constraint is added. This process of incrementally adding constraints is continued until all the constraints of a FCTOP are known by the algorithm or until the maximum number of function evaluations has been reached. We refer to this second method as the dynamic method for solving FCTOPs. Results show that our algorithm performs exceptionally well in the presence of dynamic (incremental) constraints. © 1997 Taylor & Francis Group, LLC.

关键词： Arc revision Fuzzy constraint network Fuzzy constraint truth optimization Iterative descent method Microevolutionary hill-climbing

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Solving randomly generated fuzzy constraint networks using evolutionary/systematic hill-climbing

Solving randomly generated fuzzy constraint networks using e...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： J. Bowen G. Dozier Department of Computer Science National University of Ireland Cork Cork Ireland NASA Center for Autonomous Control EngineeringDepartment of Computer Science North Carolina Agriculture and Technical State University Greensboro NC USA

This paper introduces an evolutionary/systematic hybrid which combines the concept of evolutionary hill-climbing search with the systematic search concept of arc revision to form a hybrid that quickly find solutions to fuzzy constraint satisfaction problems. This hybrid outperforms a modified version of a well known hill-climber, the iterative descent method, on a test suite of 500 randomly generated fuzzy constraint networks.

关键词： Fuzzy systems Fuzzy sets computer science Testing nasa control engineering Fuzzy control Evolutionary computation Robustness Machine learning

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