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Benchmarking Optimization Algorithms: An Open Source Framework for the Traveling Salesman Problem
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| Pengarang | : | - |
| Nama Majalah/Jurnal | : | IEEE Computational intelligence |
| Volume / Edisi | : | 9 (No. 3) |
| Halaman | : | 40-52 |
| Abstrak | : | We introduce an experimentation procedure for evaluating and comparing optimization algorithms based on the Traveling Salesman Problem (TSP). We argue that end-of-run results alone do not give sufficient information about an algorithm's performance, so our approach analyzes the algorithm's progress over time. Comparisons of performance curves in diagrams can be formalized by comparing the areas under them. Algorithms can be ranked according to a performance metric. Rankings based on different metrics can then be aggregated into a global ranking, which provides quick overview of the quality of algorithms in comparison. An open source software framework, the TSP Suite, applies this experimental procedure to the TSP. The framework can support researchers in implementing TSP solvers, unit testing them, and running experiments in a parallel and distributed fashion. It also has an evaluator component, which implements the proposed evaluation process and produces detailed reports. We test the approach by using the TSP Suite to benchmark several local search and evolutionary computation methods. This results in a large set of baseline data, which will be made available to the research community. Our experiments show that the tested pure global optimization algorithms are outperformed by local search, but the best results come from hybrid algorithms. |
Computational Intelligence Challenges and Applications on Large-Scale Astronomical Time Series Databases
| Pengarang | : | Huijse, Pablo,Estévez, Pablo A.,Protopapas, Pavlos,Principe, Jose C.,Zegers, Pablo |
| Nama Majalah/Jurnal | : | IEEE Computational intelligence |
| Volume / Edisi | : | 9 (No. 3) |
| Halaman | : | 27-39 |
| Abstrak | : | ime-domain astronomy (TDA) is facing a paradigm shift caused by the exponential growth of the sample size, data complexity and data generation rates of new astronomical sky surveys. For example, the Large Synoptic Survey Telescope (LSST), which will begin operations in northern Chile in 2022, will generate a nearly 150 Petabyte imaging dataset of the southern hemisphere sky. The LSST will stream data at rates of 2 Terabytes per hour, effectively capturing an unprecedented movie of the sky. The LSST is expected not only to improve our understanding of time-varying astrophysical objects, but also to reveal a plethora of yet unknown faint and fast-varying phenomena.To cope with a change of paradigm to data-driven astronomy, the fields of astroinformatics and astrostatistics have been created recently. The new data-oriented paradigms for astronomy combine statistics, data mining, knowledge discovery, machine learning and computational intelligence, in order to provide the automated and robust methods needed for the rapid detection and classification of known astrophysical objects as well as the unsupervised characterization of novel phenomena. In this article we present an overview of machine learning and computational intelligence applications to TDA. Future big data challenges and new lines of research in TDA, focusing on the LSST, are identified and discussed from the viewpoint of computational intelligence/ machine learning. Interdisciplinary collaboration will be required to cope with the challenges posed by the deluge of astronomical data coming from the LSST. |
The Emerging "Big Dimensionality"
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | IEEE Computational intelligence |
| Volume / Edisi | : | 9 (No. 3) |
| Halaman | : | 14-26 |
| Abstrak | : | The world continues to generate quintillion bytes of data daily, leading to the pressing needs for new efforts in dealing with the grand challenges brought by Big Data. Today, there is a growing consensus among the computational intelligence communities that data volume presents an immediate challenge pertaining to the scalability issue. However, when addressing volume in Big Data analytics, researchers in the data analytics community have largely taken a one-sided study of volume, which is the "Big Instance Size" factor of the data. The flip side of volume which is the dimensionality factor of Big Data, on the other hand, has received much lesser attention. This article thus represents an attempt to fill in this gap and places special focus on this relatively under-explored topic of "Big Dimensionality", wherein the explosion of features (variables) brings about new challenges to computational intelligence. We begin with an analysis on the origins of Big Dimensionality. The evolution of feature dimensionality in the last two decades is then studied using popular data repositories considered in the data analytics and computational intelligence research communities. Subsequently, the state-of-the-art feature selection schemes reported in the field of computational intelligence are reviewed to reveal the inadequacies of existing approaches in keeping pace with the emerging phenomenon of Big Dimensionality. Last but not least, the "curse and blessing of Big Dimensionality" are delineated and deliberated. |
Computational Intelligence in Big Data
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | IEEE Computational intelligence |
| Volume / Edisi | : | 9 (No. 3) |
| Halaman | : | 12-13 |
| Abstrak | : | - |
IEEE CIFEr 2014-Leading Forum on Computational Finance and Economics Research in Academia and Industry
| Pengarang | : | Lipton, Alexander,Serguieva, Antoaneta,Xin Yao |
| Nama Majalah/Jurnal | : | IEEE Computational intelligence |
| Volume / Edisi | : | 9 (No. 3) |
| Halaman | : | 8-11 |
| Abstrak | : | - |
CIS Publication Spotlight
| Pengarang | : | Derong Liu,Chin-Teng Lin,Greenwood, Garry,Lucas, Simon,Zhang, Zhengyou |
| Nama Majalah/Jurnal | : | IEEE Computational intelligence |
| Volume / Edisi | : | 9 (No. 3) |
| Halaman | : | 6-7 |
| Abstrak | : | - |
A Memetic Algorithm for Resource Allocation Problem Based on Node-Weighted Graphs
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | IEEE Computational intelligence |
| Volume / Edisi | : | 9 (No. 2) |
| Halaman | : | 58-69 |
| Abstrak | : | - |
Jumping NLP Curves: A Review of Natural Language Processing Research
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | IEEE Computational intelligence |
| Volume / Edisi | : | 9 (No. 2) |
| Halaman | : | 48-57 |
| Abstrak | : | - |
Muscle Fatigue Tracking with Evoked EMG via Recurrent Neural Network: Toward Personalized Neuroprosthetics
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | IEEE Computational intelligence |
| Volume / Edisi | : | 9 (No. 2) |
| Halaman | : | 38-46 |
| Abstrak | : | -One of the challenging issues in computational rehabilitation is to muscle that there is a large variety of patient situations depending on the type of neurological disorder. Human characteristics are basically subject specific and time variant; for instance, neuromuscular dynamics may vary due fatigue. To tackle such patient specificity and time-varying characteristics, a robust bio-signal processing and a precise model-based control which can manage the nonlinearity and time variance of the system, would bring break-through and new modality toward computational intelligence (CI) based rehabilitation technology and personalized neuroprosthetics. Functional electrical stimulation (FES) is a useful technique to assist restoring motor capability of spinal cord injured (SCI) patients by delivering electrical pulses to paralyzed muscles. However, muscle fatigue constraints the application of FES as it results in the time-variant muscle response. To perform adaptive closedloop FES control with actual muscle response feedback taken into account, muscular torque is essential to be estimated accurately. However, inadequacy of the implantable torque sensor limits the direct measurement of the time-variant torque at the joint. This motivates the development of methods to estimate muscle torque from bio-signals that can be measured. Evoked electromyogram (eEMG) has been found to be highly correlated with FES-induced torque under various muscle conditions, indicating that it can be used for torque/force prediction. A nonlinear ARX (NARX) type model is preferred to track the relationship between eEMG and stimulated muscular torque. This paper presents a NARX recurrent neural network (NARX-RNN) model for identification/prediction of FES-induced muscular dynamics with eEMG. The NARX-RNN model may possess novelty of robust prediction performance. Due to the difficulty of choosing a proper forgetting factor of Kalman filter for predicting time-variant torque with eEMG, the presented NARX-RNN could be considered as an alternative muscular torque predictor. Data collected from five SCI patients is used to evaluate the proposed NARX-RNN model, and the results show promising estimation performances. In addition, the general importance regarding CI-based motor function modeling is introduced along with its potential impact in the rehabilitation domain. The issue toward personalized neuroprosthetics is discussed in detail with the potential role of CI-based identification and the benefit for motor-impaired patient community. |
Landmark-Based Methods for Temporal Alignment of Human Motions
| Pengarang | : | Paul Wai Hing Chung,Pablo Fernández de Dios,Meng, Qinggang |
| Nama Majalah/Jurnal | : | IEEE Computational intelligence |
| Volume / Edisi | : | 9 (No. 2) |
| Halaman | : | 29-37 |
| Abstrak | : | Considerable current research is focusing on the subject of "Wellbeing and aging", which covers a wide range of solutions to help encourage elderly people to engage on routine physical exercise. In particular, elderly communities in nursing homes are usually involved in activities on rehabilitation, daily exercises and health tracking. In order to implement an automated system for elderly exercise monitoring, human motion analysis should be efficiently performed in an affordable way, and delivered in a way that the users understand. In this paper, a general framework for comparison of fitness performances in the context of basic stand-up physical activities is presented. The Microsoft Kinect device is used for motion capture. A method for key body pose prediction on human activities based on multi-class C4.5, SVM, Naive Bayes and AdaBoost classifiers is first introduced, followed by a cluster analysis to refine the obtained results. The performances achieved with the different techniques and parameters are then analyzed and the best configuration compares favorably against the DTW and HACA algorithms. |