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Hybrid firefly based Simultaneous Gene Selection and Cancer Classification using Support Vector Machines and Random Forests

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7th International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA 2012)

作者： Srivastava, Atulji Chakrabarti, Saurabh Das, Subrata Ghosh, Shameek Jayaraman, V. K. CDAC Evolutionary Comp & Image Proc Grp Pune Univ Campus Pune 411007 Maharashtra India Padmashree Dr DY Patil Univ Dr DY Patil Biotechnol & Bioinformat Inst Pune Maharashtra India Univ Pune Dept Comp Sci Pune 411007 Maharashtra India

ISBN: (纸本)9788132210375

Microarray cancer gene expression datasets are high dimensional and thus complex for efficient computational analysis. In this study, we address the problem of simultaneous gene selection and robust classification of cancerous samples by presenting two hybrid algorithms, namely Discrete firefly based Support Vector Machines(DFA-SVM) and DFA-Random Forests(DFA-RF) with weighted gene ranking as heuristics. The performances of the algorithms are then tested using two cancer gene expression datasets retrieved from the Kent Ridge Biomedical Dataset Repository. Our results show that both DFA-SVM and DFA-RF can help in extracting more informative genes aiding to building high performance prediction models.

关键词： Cancer Classification Weighted Gene Ranking firefly algorithm Support Vector Machines Random Forests

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Bat algorithm: literature review and applications

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2013年第3期5卷 141-+页

作者： Yang, Xin-She He, Xingshi Middlesex Univ Sch Sci & Technol London NW4 4BT England Xian Polytech Univ Sch Sci Xian 710048 Peoples R China

Bat algorithm (BA) is a bio-inspired algorithm developed by Xin-She Yang in 2010 and BA has been found to be very efficient. As a result, the literature has expanded significantly in the last three years. This paper provides a timely review of the bat algorithm and its new variants. A wide range of diverse applications and case studies are also reviewed and summarised briefly here. In addition, we also discuss the essence of an algorithm and the links between algorithms and self-organisation. Further research topics are also discussed.

关键词： algorithm bat algorithm cuckoo search firefly algorithm eagle strategy nature-inspired algorithm optimisation metaheuristics

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Feature Selection of Motor Imagery EEG Signals Using firefly Temporal Difference Q-Learning and Support Vector Machine

Feature Selection of Motor Imagery EEG Signals Using Firefly...

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4th International Conference on Swarm, Evolutionary, and Memetic Computing (SEMCCO)

作者： Bhattacharyya, Saugat Rakshit, Pratyusha Konar, Amit Tibarewala, D. N. Janarthanan, Ramadoss Dept. of Electronics and Telecommunication Engg. Jadavpur University Kolkata India School of Bioscience and Engg. Jadavpur University Kolkata India Computer Science Dept. Jadavpur University Kolkata India Jadavpur University Kolkata India TJS Engineering College Chennai India

ISBN: (纸本)9783319037554;9783319037561

Electroencephalograph (EEG) based Brain-computer Interface (BCI) research provides a non-muscular communication to drive assistive devices using movement related signals, generated from the motor activation areas of the brain. The dimensions of the feature vector play an important role in BCI research, which not only increases the computational time but also reduces the accuracy of the classifiers. In this paper, we aim to reduce the redundant features of a feature vector obtained from motor imagery EEG signals to improve their corresponding classification. In this paper we have proposed a feature selection method based on firefly algorithm and Temporal Difference Q-Learning. Here, we have applied our proposed method to the wavelet transform features of a standard BCI competition dataset. Support Vector Machines have been employed to determine the fitness function of the proposed method and obtain the resultant classification accuracy. We have shown that the accuracy of the reduced feature are considerably higher than the original features. This paper also demonstrates the superiority of the new method to its competitor algorithms.

关键词： Brain-Computer Interfacing Electroencephalography firefly algorithm Temporal Difference Q-Learning Support Vector Machines Wavelet Transforms

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A framework for self-tuning optimization algorithm

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NEURAL COMPUTING & APPLICATIONS 2013年第7-8期23卷 2051-2057页

作者： Yang, Xin-She Deb, Suash Loomes, Martin Karamanoglu, Mehmet Middlesex Univ Sch Sci & Technol London NW4 4BT England Cambridge Inst Technol Ranchi 835103 Jharkhand India

The performance of any algorithm will largely depend on the setting of its algorithm-dependent parameters. The optimal setting should allow the algorithm to achieve the best performance for solving a range of optimization problems. However, such parameter tuning itself is a tough optimization problem. In this paper, we present a framework for self-tuning algorithms so that an algorithm to be tuned can be used to tune the algorithm itself. Using the firefly algorithm as an example, we show that this framework works well. It is also found that different parameters may have different sensitivities and thus require different degrees of tuning. Parameters with high sensitivities require fine-tuning to achieve optimality.

关键词： algorithm firefly algorithm Parameter tuning Optimization Metaheuristic Nature-inspired algorithm

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Capacitated Facility Location Problem in random fuzzy environment: Using (α, β)-cost minimization model under the Hurwicz criterion

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2013年第4期25卷 953-964页

作者： Rahmani, Arsalan Yousefikhoshbakht, Majid Amirkabir Univ Technol Dept Math & Comp Sci Tehran Iran Islamic Azad Univ Hamedan Branch Young Researchers Club Hamadan Iran

Capacitated Facility Location Problem (CFLP) as one of the most important problems in operations research is nowadays widely used in real life. Although many researchers have studied the CFLP in a deterministic environment, their proposed models cannot satisfactorily accommodate various customer demands in the real world. Thus, this paper focuses on the CFLP random fuzzy environment using (alpha, beta)-cost minimization model under the Hurwicz criterion. Furthermore, it is proved that this model can deal with various CFLPs in random, fuzzy and random fuzzy environments. In order to solve this model, the simplex algorithm, fuzzy simulations and a firefly algorithm are integrated to produce a hybrid intelligent algorithm. Finally, some numerical examples are presented to illustrate the effectiveness of the proposed algorithm.

关键词： Random fuzzy environment fuzzy simulation firefly algorithm Capacitated Facility Location Problem Hurewicz criterion

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AGC of a Multi-area Hydrothermal System Using Thyristor Controlled Series Capacitor

AGC of a Multi-area Hydrothermal System Using Thyristor Cont...

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10th Annual Conference of the IEEE-India-Council (INDICON)

作者： Saikia, Lalit Chandra Debbarma, Sanjoy Sinha, N. Dash, Puja Natl Inst Technol Silchar Dept Elect Engn Silchar India

ISBN: (纸本)9781479922741;9781479922758

This paper presents automatic generation control (AGC) of two and three unequal area hydrothermal systems. The thermal areas of the systems are equipped with single reheat turbine and hydro area is with electric governor. Appropriate Generation Rate Constraint (GRC) has been considered in each area. System dynamics has been obtained by considering 1% step load perturbation (SLP) in area. Performances of several classical controllers such as Integral (I), Proportional-Integral (PI), Integral-Derivative (ID), Proportional-Integral-Derivative (PID) and Integral-Double Derivative (IDD) has been evaluated and compared in systems. firefly algorithm (FA) has been used for optimization of different parameters. Investigations reveal that IDD controller provides better dynamic than the others in both systems. In the three area system, thyristor controlled series capacitor (TCSC) have been used in tie-line. Investigations expose that using TCSC, the oscillations and peak deviations in the responses can be reduced. Simultaneous optimization of governor speed regulation parameter (R-i) and IDD controller gains with or without TCSC provides higher values of R-i, thereby leading to economic and convenient realization of Governors.

关键词： Automatic Generation Control firefly algorithm Integral-Double Derivative controller Thyristor - controlled series capacitor

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Solar PV Power Generation Forecast Using a Hybrid Intelligent Approach

Solar PV Power Generation Forecast Using a Hybrid Intelligen...

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General Meeting of the IEEE-Power-and-Energy-Society (PES)

作者： Haque, Ashraf U. Nehrir, M. Hashem Mandal, Paras Montana State Univ Elect & Comp Engn Dept Bozeman MT 59717 USA Univ Texas El Paso Dept Ind Mfg & Syst Engn El Paso TX 79968 USA

ISBN: (纸本)9781479913039

A significant role of a smart grid is to substantially increase the penetration of environmentally-friendly renewable energy sources, such as solar photovoltaic (PV) power. One of the major challenges associated with the integration of PV power into the grid is the intermittent and uncontrollable nature of PV power output. Therefore, developing a reliable forecasting algorithm can be extremely beneficial in system planning and market operation of grid-connected PV systems. This paper presents a novel hybrid intelligent algorithm for short-term forecasting of PV-generated power. The algorithm uses a combination of a data filtering technique based on wavelet transform (WT) and a soft computing model based on fuzzy ARTMAP (FA) network, which is optimized using an optimization technique based on firefly (FF) algorithm.

关键词： firefly algorithm fuzzy ARTMAP PV power forecasting wavelet transform

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Non Uniform Element Angular Spacing of a Hemi Spherical Antenna Array for Vector Optimization Employing Natural Computing Techniques

Non Uniform Element Angular Spacing of a Hemi Spherical Ante...

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10th Annual Conference of the IEEE-India-Council (INDICON)

作者： Kumar, A. T. Praveen Subhashini, K. R. Natl Inst Technol Dept Elect Engn Rourkela India

ISBN: (纸本)9781479922741;9781479922758

This paper presents a pattern synthesis method based on two powerful tools of the Evolutionary computing i.e Differential Evolution, Invasive weed optimization and firefly technique, to provide a significant side lobe reduction. The Antenna elements are embedded on a hemispherical array that leads to one of the geometry of a conformal shape. The array elements are excited with uniform amplitude excitation and progressively phased. The calculation of the element position is presented. The hemispherical array geometry is designed to meet multi objective criteria such as to achieve the desired side lobe reduction, null point detection and main beam enhancement. The exploitative changes of the Invasive Weed Optimization, Differential Evolution and firefly algorithms have been applied for this array pattern synthesis. Simulation results of the conformal array design have been presented to illustrate the effectiveness and robustness of the Weed technique on the Differential Evolution and firefly algorithm. Empirical results clearly demonstrate the ability of IWO outperforming its competitors. By this method, arrays have the ability to produce the desired radiation pattern and could satisfy requirements for many applications.

关键词： Hemispherical antenna array Invasive weed optimization Differential Evolution firefly algorithm Pattern synthesis

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A Novel Hybrid Model for Network Intrusion Detection

A Novel Hybrid Model for Network Intrusion Detection

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International Conference on Green Computing, Communication and Conservation of Energy (ICGCE)

作者： Tiwari, Shobhit Roy, Sanjiban Sekhar Charaborty, Saptarshi Kumar, Anugrah Schneider Elect Bangalore Karnataka India VIT Univ Sch Comp Sci & Engn Vellore Tamil Nadu India

ISBN: (纸本)9781467361255

With the ever increasing importance and usage of Wireless Sensor Networks (WSN), security of the network has become a major concern. As more and more crucial applications of Wireless Sensor Networks are being found, more needs to be done to ensure the safety of the data and network. Intrusion detection is a method to find and establish appropriate defenses against any malicious attack on the network. However standard intrusion detection practices alone cannot ensure the safety of the wireless sensor networks due to various constraints on the power, memory and battery life of the sensors and on many other uncertain factors. In this paper we present an intrusion detection scheme using Neural Networks, Rough sets and firefly algorithm (FA).

关键词： intrusion detection firefly algorithm neural network rough set

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Automatic Generation Control of Multi-Area System Using Two Degree of Freedom Fractional Order PID Controller: A Preliminary Study

Automatic Generation Control of Multi-Area System Using Two ...

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IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC)

作者： Debbarma, Sanjoy Saikia, Lalit Chandra Sinha, Nidul Natl Inst Technol Meghalaya Shillong Meghalaya India Natl Inst Technol Silchar Silchar Assam India

ISBN: (纸本)9781479925223

In this paper, Two-Degree-of-Freedom-Fractional Order-PID (2-DOF-FOPID) Controller is presented for automatic generation control of an interconnected three unequal area thermal system. Performances of different fractional order (FO) based classical controllers such as FOI, FOPI and FOPID are studied and compared with proposed 2-DOF-FOPID controller. Recently developed metaheuristic nature-inspired algorithm known as firefly algorithm (FA) are used for the simultaneous optimization of several parameters of the controllers and speed regulation parameter (R) of the governor. Simulation results clearly reveal the superiority of the proposed 2-DOF-FOPID controller. Further, the performances of proposed controller are tested against higher degree of perturbation. It is observed that 2-DOF-FOPID controller perform far better than aforesaid FO controllers.

关键词： Automatic generation control Fractional order controller firefly algorithm 2-DOF-FOPID controller

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