The Experts below are selected from a list of 11748 Experts worldwide ranked by ideXlab platform
Kumlachew M. Woldemariam - One of the best experts on this subject based on the ideXlab platform.
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vaccine enhanced Artificial Immune System for multimodal function optimization
World Congress on Computational Intelligence, 2008Co-Authors: Kumlachew M. WoldemariamAbstract:This paper proposes the use of vaccine to promote exploration in the search space for solving multimodal function optimization problems using Artificial Immune System. In this method, first we divide the decision space into equal subspaces. Vaccine is then extracted randomly from each subspace. A few of these antigens are then injected into the algorithm to enhance the exploration of global and local optima. The vaccine is introduced in the form of suppressed antibodies. The goal of this process is to allocate the available antibodies at unexplored areas. Using this biologically motivated notion we design the vaccine enhanced Artificial Immune System for multimodal function optimization.
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IEEE Congress on Evolutionary Computation - Vaccine enhanced Artificial Immune System for multimodal function optimization
2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), 2008Co-Authors: Kumlachew M. WoldemariamAbstract:This paper proposes the use of vaccine to promote exploration in the search space for solving multimodal function optimization problems using Artificial Immune System. In this method, first we divide the decision space into equal subspaces. Vaccine is then extracted randomly from each subspace. A few of these antigens are then injected into the algorithm to enhance the exploration of global and local optima. The vaccine is introduced in the form of suppressed antibodies. The goal of this process is to allocate the available antibodies at unexplored areas. Using this biologically motivated notion we design the vaccine enhanced Artificial Immune System for multimodal function optimization.
Sunita Bansal - One of the best experts on this subject based on the ideXlab platform.
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Artificial Immune System Approach for Multi Objective Optimization
International journal of advanced computer science, 2014Co-Authors: Garima Singh, Sunita BansalAbstract:This paper presents a modified Artificial Immune System based approach to solve multi objective optimization problems. The main objective of the solution of multi objective optimization problem is to help a human decision maker in taking his/her decision for finding the most preferred solution as the final result. This Artificial Immune System algorithm makes use of mechanism inspired by vertebrate Immune System and clonal selection principle. In the present model crossover mechanism is integrated into traditional Artificial Immune System algorithm based on clonal selection theory. The Algorithm is proposed with real parameters value not binary coded parameters. Only non dominated individual and feasible best antibodies will add to the memory set. This algorithm will be used to solve various real life engineering multi-objective optimization problems. The attraction for choosing the Artificial Immune System to develop algorithm was that if an adaptive pool of antibodies can produce 'intelligent' behavior, we can use this power of computation to tackle the problem of multi objective optimization.
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Detecting Online Credit Card Fraud Using Artificial Immune System
2014Co-Authors: Shagufta Warsi, Sunita BansalAbstract:Artificial Immune System is inspired by Biological Immune System which belongs to Artificial Intelligence family. During the past years they have attracted lot of interest in researchers to use it in various applications. This work represents survey on Artificial Immune System as an application for detecting online credit card fraud, with a survey on Immune based algorithms including the description about the proposed work.
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Artificial Immune System Approach for Multi Objective Optimization
Computer Engineering and Intelligent Systems, 2013Co-Authors: Garima Singh, Sunita BansalAbstract:This paper presents a modified Artificial Immune System based approach to solve multi objective optimization problems. The main objective of the solution of multi objective optimization problem is to help a human decision maker in taking his/her decision for finding the most preferred solution as the final result. This Artificial Immune System algorithm makes use of mechanism inspired by vertebrate Immune System and clonal selection principle. In the present model crossover mechanism is integrated into traditional Artificial Immune System algorithm based on clonal selection theory. The Algorithm is proposed with real parameters value not binary coded parameters. Only non dominated individual and feasible best antibodies will add to the memory set. This algorithm will be used to solve various real life engineering multi-objective optimization problems. The attraction for choosing the Artificial Immune System to develop algorithm was that if an adaptive pool of antibodies can produce 'intelligent' behavior, we can use this power of computation to tackle the problem of multi objective optimization. Keywords: Artificial Immune System, Clonal Selection Theory, Multi Objective Optimization, Pareto Optimal.
Marc Schoenauer - One of the best experts on this subject based on the ideXlab platform.
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An Artificial Immune System for offline isolated handwritten arabic character recognition
Evolving Systems, 2018Co-Authors: Chaouki Boufenar, Mohamed Batouche, Marc SchoenauerAbstract:Character recognition plays an important role in the modern world. In recent years, character recognition Systems for different languages has gain importance. The recognition of Arabic writing is still an important challenge due to its cursive nature and great topological variability. The Artificial Immune System is a supervised learning technique that embodies the concepts of natural immunity to cope with complex classification problems. The objective of this research is to investigate the applicability of an Artificial Immune System in Offline Isolated Handwritten Arabic Characters. The developed System is composed of three main modules: preprocessing, feature extraction and recognition. The System was trained and tested with ten-fold cross-validation technique on an original realistic database that we built from the well-known IFN/ENIT benchmark. Parameter tuning was performed with a grid-search algorithm with leave-one-out cross-validation. The obtained results of the proposed System are promising with a classification rate of 93.25% and often outperform most well-known classifiers from Scikit Learn Library.
Jennifer Davis - One of the best experts on this subject based on the ideXlab platform.
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Artificial-Immune-System-Based Detection Scheme for Aircraft Engine Failures
Journal of Guidance Control and Dynamics, 2011Co-Authors: Mario G. Perhinschi, Hever Moncayo, Jennifer Davis, Jaclyn Porter, W. Scott WayneAbstract:A detection scheme based on the Artificial Immune System paradigm was developed for specific classes of aircraft jet engine actuator and sensor failures, including throttle, burner fuel-flow valve, variable nozzle-area actuator, variablemixer-area actuator, low-pressure spool-speed sensor, low-pressure turbine exit static-pressure sensor, and mixer pressure-ratio sensor. The NASA Modular Aero-Propulsion System Simulation model was linearized and interfaced with a supersonic fighter aircraft model and a motion-based flight simulator, providing the adequate framework for development and testing. Several engine actuator and sensor failures weremodeled and implemented into this simulation environment. A five-dimensional hyperspace was determined to build the self within the Artificial Immune System paradigm for detection purposes. The Artificial Immune System interactive design environment based on evolutionary algorithms developed at West Virginia University was used for data processing, detector generation, and optimization. Flight-simulation data for System development and testing were acquired through experiments in a motion-based flight simulator over extended areas of the flight envelope. The performance of the Artificial-Immune-System-based detection scheme was evaluated in terms of detection rates and false alarms. Results show that the Artificial-Immune-System-based approach has excellent potential for the detection of all of the classes of engine failures considered.
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Artificial Immune System – Based Aircraft Failure Evaluation over Extended Flight Envelope
Journal of Guidance Control and Dynamics, 2011Co-Authors: Hever Moncayo, Mario G. Perhinschi, Jennifer DavisAbstract:This paper describes the design, development, and flight-simulation testing of an Artificial-Immune-System-based approach for the evaluation of different aircraft subSystem failures/damages. The evaluation consists of the estimation of the magnitude/severity of the failure and the prediction of the achievable states, leading to an overall assessment of the effects of the failure on reducing the flight envelope. A supersonic fighter model is used, which includes model-following adaptive control laws based on nonlinear dynamic inversion and Artificial neural network augmentation. Data collected from a motion-based flight simulator were used to define the self for a wide area of the flight envelope and to test and validate the proposed approach. Example results are presented for failure-magnitude evaluation and flight-envelope-reduction prediction for abnormal conditions affecting sensors, actuators, engine, and wing structure. Successful failure detection and identification are assumed before evaluation. The results show the capabilities of the Artificial-Immune-System-based scheme to evaluate the severity of the failure and to predict the reduction of the flight envelope in a general manner.
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Artificial Immune System based aircraft failure evaluation over extended flight envelope
Journal of Guidance Control and Dynamics, 2010Co-Authors: Hever Moncayo, Mario G. Perhinschi, Jennifer DavisAbstract:This paper describes the design, development, and flight-simulation testing of an Artificial-Immune-System-based approach for the evaluation of different aircraft subSystem failures/damages. The evaluation consists of the estimation of the magnitude/severity of the failure and the prediction of the achievable states, leading to an overall assessment of the effects of the failure on reducing the flight envelope. A supersonic fighter model is used, which includes model-following adaptive control laws based on nonlinear dynamic inversion and Artificial neural network augmentation. Data collected from a motion-based flight simulator were used to define the self for a wide area of the flight envelope and to test and validate the proposed approach. Example results are presented for failure-magnitude evaluation and flight-envelope-reduction prediction for abnormal conditions affecting sensors, actuators, engine, and wing structure. Successful failure detection and identification are assumed before evaluation. The results show the capabilities of the Artificial-Immune-System-based scheme to evaluate the severity of the failure and to predict the reduction of the flight envelope in a general manner.
Yang Jian-hua - One of the best experts on this subject based on the ideXlab platform.
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Intellectualized diagnosis model for the missile fault based on Artificial Immune System
Computer Engineering, 2005Co-Authors: Guo Xiao-sheng, Yang Jian-huaAbstract:A conception of missile intelligent fault diagnosis technology based on Artificial Immune System was presented,the fault diagnosis cell model and the fault diagnosis gene model were analyzed.How to create and evolve the fault diagnosis gene model was introduced,and the intelligent fault diagnosis principle based on Artificial Immune System was discussed.
