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Showing 1201 - 1400 of 2349 Journals sorted alphabetically
J. of Clinical Monitoring and Computing     Hybrid Journal   (Followers: 1, SJR: 0.661, h-index: 37)
J. of Clinical Psychology in Medical Settings     Hybrid Journal   (Followers: 14, SJR: 0.46, h-index: 34)
J. of Cluster Science     Hybrid Journal   (SJR: 0.416, h-index: 31)
J. of Coal Science and Engineering (China)     Hybrid Journal   (SJR: 0.188, h-index: 8)
J. of Coastal Conservation     Hybrid Journal   (Followers: 5, SJR: 0.474, h-index: 25)
J. of Coatings Technology and Research     Hybrid Journal   (Followers: 5, SJR: 0.425, h-index: 25)
J. of Combinatorial Optimization     Hybrid Journal   (Followers: 6, SJR: 1.093, h-index: 34)
J. of Communications Technology and Electronics     Hybrid Journal   (Followers: 2, SJR: 0.29, h-index: 16)
J. of Community Genetics     Hybrid Journal   (SJR: 0.727, h-index: 14)
J. of Community Health     Hybrid Journal   (Followers: 8, SJR: 0.921, h-index: 44)
J. of Comparative Physiology A: Neuroethology, Sensory, Neural, and Behavioral Physiology     Hybrid Journal   (Followers: 9, SJR: 1.087, h-index: 74)
J. of Comparative Physiology B : Biochemical, Systemic, and Environmental Physiology     Hybrid Journal   (Followers: 5, SJR: 1.126, h-index: 59)
J. of Compassionate Health Care     Open Access   (Followers: 3)
J. of Computational Analysis and Applications     Hybrid Journal   (SJR: 0.291, h-index: 19)
J. of Computational Electronics     Hybrid Journal   (Followers: 4, SJR: 0.511, h-index: 20)
J. of Computational Neuroscience     Hybrid Journal   (Followers: 25, SJR: 1.068, h-index: 60)
J. of Computer and Systems Sciences Intl.     Hybrid Journal   (SJR: 0.27, h-index: 13)
J. of Computer Science and Technology     Open Access   (Followers: 5, SJR: 0.437, h-index: 31)
J. of Computer Virology and Hacking Techniques     Hybrid Journal   (Followers: 6, SJR: 0.151, h-index: 2)
J. of Computer-Aided Molecular Design     Hybrid Journal   (Followers: 3, SJR: 0.995, h-index: 78)
J. of Computers in Education     Hybrid Journal   (Followers: 12)
J. of Computing in Higher Education     Hybrid Journal   (Followers: 12, SJR: 0.363, h-index: 21)
J. of Consumer Policy     Hybrid Journal   (Followers: 7, SJR: 0.704, h-index: 30)
J. of Contemporary Mathematical Analysis     Hybrid Journal   (SJR: 0.237, h-index: 5)
J. of Contemporary Physics (Armenian Academy of Sciences)     Hybrid Journal   (Followers: 10, SJR: 0.197, h-index: 6)
J. of Contemporary Psychotherapy     Hybrid Journal   (Followers: 5, SJR: 0.397, h-index: 23)
J. of Control Theory and Applications     Hybrid Journal   (Followers: 2, SJR: 0.359, h-index: 19)
J. of Control, Automation and Electrical Systems     Hybrid Journal   (Followers: 9, SJR: 0.231, h-index: 9)
J. of Crop Science and Biotechnology     Hybrid Journal   (Followers: 3)
J. of Cross-Cultural Gerontology     Hybrid Journal   (Followers: 6, SJR: 0.631, h-index: 29)
J. of Cryptographic Engineering     Partially Free   (Followers: 4, SJR: 0.989, h-index: 11)
J. of Cryptology     Hybrid Journal   (Followers: 3, SJR: 1.443, h-index: 55)
J. of Cultural Economics     Hybrid Journal   (Followers: 2, SJR: 0.539, h-index: 29)
J. of Database Marketing & Customer Strategy Management     Hybrid Journal   (Followers: 10, SJR: 0.149, h-index: 8)
J. of Derivatives & Hedge Funds     Hybrid Journal   (Followers: 7, SJR: 0.114, h-index: 5)
J. of Developmental and Physical Disabilities     Hybrid Journal   (Followers: 7, SJR: 0.574, h-index: 29)
J. of Digital Imaging     Hybrid Journal   (Followers: 6, SJR: 0.578, h-index: 35)
J. of Direct Data and Digital Marketing Practice     Hybrid Journal   (Followers: 8, SJR: 0.154, h-index: 6)
J. of Dynamical and Control Systems     Hybrid Journal   (Followers: 1, SJR: 0.4, h-index: 26)
J. of Dynamics and Differential Equations     Hybrid Journal   (SJR: 1.418, h-index: 31)
J. of Earth Science     Hybrid Journal   (Followers: 9, SJR: 0.483, h-index: 16)
J. of Earth System Science     Open Access   (Followers: 49, SJR: 0.448, h-index: 32)
J. of East Asian Linguistics     Hybrid Journal   (Followers: 6, SJR: 0.537, h-index: 20)
J. of Echocardiography     Hybrid Journal   (Followers: 5, SJR: 0.22, h-index: 3)
J. of Ecology and Environment     Open Access   (Followers: 1)
J. of Economic Growth     Hybrid Journal   (Followers: 21, SJR: 3.273, h-index: 63)
J. of Economic Interaction and Coordination     Hybrid Journal   (SJR: 0.263, h-index: 12)
J. of Economics     Hybrid Journal   (Followers: 12, SJR: 0.418, h-index: 23)
J. of Economics and Finance     Hybrid Journal   (Followers: 5, SJR: 0.272, h-index: 19)
J. of Educational Change     Hybrid Journal   (Followers: 6, SJR: 0.961, h-index: 21)
J. of Elasticity     Hybrid Journal   (Followers: 6, SJR: 0.851, h-index: 45)
J. of Electroceramics     Hybrid Journal   (SJR: 0.577, h-index: 57)
J. of Electronic Materials     Hybrid Journal   (Followers: 4, SJR: 0.609, h-index: 75)
J. of Electronic Testing     Hybrid Journal   (Followers: 2, SJR: 0.372, h-index: 27)
J. of Electronics (China)     Hybrid Journal   (Followers: 4, SJR: 0.112, h-index: 9)
J. of Elementary Science Education     Hybrid Journal   (Followers: 8)
J. of Elliptic and Parabolic Equations     Hybrid Journal  
J. of Engineering Mathematics     Hybrid Journal   (SJR: 0.347, h-index: 37)
J. of Engineering Physics and Thermophysics     Hybrid Journal   (Followers: 1, SJR: 0.288, h-index: 11)
J. of Engineering Research     Open Access   (Followers: 1, SJR: 0.145, h-index: 5)
J. of Engineering Thermophysics     Hybrid Journal   (Followers: 4, SJR: 0.763, h-index: 9)
J. of Environmental Studies and Sciences     Partially Free   (Followers: 2)
J. of Ethology     Hybrid Journal   (Followers: 2, SJR: 0.609, h-index: 25)
J. of Evolution Equations     Hybrid Journal   (SJR: 0.826, h-index: 26)
J. of Evolutionary Biochemistry and Physiology     Hybrid Journal   (Followers: 1, SJR: 0.145, h-index: 11)
J. of Evolutionary Economics     Hybrid Journal   (Followers: 7, SJR: 0.492, h-index: 52)
J. of Experimental and Theoretical Physics     Hybrid Journal   (Followers: 3, SJR: 0.458, h-index: 39)
J. of Experimental Criminology     Hybrid Journal   (Followers: 50, SJR: 1.445, h-index: 28)
J. of Failure Analysis and Prevention     Hybrid Journal   (Followers: 5, SJR: 0.261, h-index: 15)
J. of Family and Economic Issues     Hybrid Journal   (Followers: 3, SJR: 0.396, h-index: 32)
J. of Family Violence     Hybrid Journal   (Followers: 41, SJR: 0.639, h-index: 56)
J. of Financial Services Marketing     Hybrid Journal   (Followers: 3, SJR: 0.273, h-index: 10)
J. of Financial Services Research     Hybrid Journal   (Followers: 24, SJR: 0.572, h-index: 36)
J. of Fixed Point Theory and Applications     Hybrid Journal   (SJR: 0.644, h-index: 13)
J. of Fluorescence     Hybrid Journal   (Followers: 3, SJR: 0.465, h-index: 56)
J. of Food Measurement and Characterization     Hybrid Journal   (SJR: 0.307, h-index: 4)
J. of Food Science and Technology     Hybrid Journal   (Followers: 6, SJR: 0.441, h-index: 29)
J. of Forest Research     Hybrid Journal   (Followers: 2, SJR: 0.495, h-index: 27)
J. of Forestry Research     Hybrid Journal   (Followers: 3, SJR: 0.304, h-index: 14)
J. of Fourier Analysis and Applications     Hybrid Journal   (Followers: 1, SJR: 1.18, h-index: 42)
J. of Friction and Wear     Hybrid Journal   (Followers: 7, SJR: 0.373, h-index: 7)
J. of Fusion Energy     Hybrid Journal   (Followers: 3, SJR: 0.387, h-index: 19)
J. of Gambling Studies     Hybrid Journal   (Followers: 7, SJR: 1.171, h-index: 57)
J. of Gastroenterology     Hybrid Journal   (Followers: 11, SJR: 1.651, h-index: 88)
J. of Gastrointestinal Cancer     Hybrid Journal   (Followers: 3, SJR: 0.304, h-index: 39)
J. of Gastrointestinal Surgery     Hybrid Journal   (Followers: 10, SJR: 1.64, h-index: 99)
J. of General Internal Medicine     Hybrid Journal   (Followers: 17, SJR: 1.804, h-index: 134)
J. of General Plant Pathology     Hybrid Journal   (SJR: 0.554, h-index: 22)
J. of Genetic Counseling     Hybrid Journal   (Followers: 6, SJR: 0.902, h-index: 39)
J. of Genetics     Open Access   (Followers: 5, SJR: 0.458, h-index: 28)
J. of Geodesy     Hybrid Journal   (Followers: 8, SJR: 2.173, h-index: 56)
J. of Geographical Sciences     Hybrid Journal   (Followers: 1, SJR: 0.8, h-index: 23)
J. of Geographical Systems     Hybrid Journal   (Followers: 4, SJR: 0.822, h-index: 39)
J. of Geometric Analysis     Hybrid Journal   (Followers: 2, SJR: 1.491, h-index: 27)
J. of Geometry     Hybrid Journal   (Followers: 1, SJR: 0.272, h-index: 15)
J. of Global Optimization     Hybrid Journal   (Followers: 4, SJR: 0.992, h-index: 60)
J. of Global Policy and Governance     Hybrid Journal   (Followers: 10)
J. of Grid Computing     Hybrid Journal   (Followers: 1, SJR: 1.414, h-index: 37)
J. of Happiness Studies     Hybrid Journal   (Followers: 26, SJR: 0.881, h-index: 39)
J. of Hematopathology     Hybrid Journal   (Followers: 3, SJR: 0.2, h-index: 13)
J. of Heuristics     Hybrid Journal   (Followers: 4, SJR: 1.308, h-index: 50)
J. of High Energy Physics     Hybrid Journal   (Followers: 17, SJR: 1.052, h-index: 153)
J. of Homotopy and Related Structures     Hybrid Journal   (SJR: 0.232, h-index: 2)
J. of Housing and the Built Environment     Hybrid Journal   (Followers: 7, SJR: 0.648, h-index: 28)
J. of Huazhong University of Science and Technology [Medical Sciences]     Hybrid Journal   (SJR: 0.344, h-index: 19)
J. of Ichthyology     Hybrid Journal   (Followers: 2, SJR: 0.304, h-index: 10)
J. of Immigrant and Minority Health     Hybrid Journal   (Followers: 12, SJR: 0.759, h-index: 37)
J. of Inclusion Phenomena and Macrocyclic Chemistry     Hybrid Journal   (Followers: 1, SJR: 0.331, h-index: 46)
J. of Indian Council of Philosophical Research     Hybrid Journal  
J. of Indian Philosophy     Hybrid Journal   (Followers: 9, SJR: 0.127, h-index: 12)
J. of Industrial Microbiology and Biotechnology     Hybrid Journal   (Followers: 16, SJR: 0.966, h-index: 80)
J. of Industry, Competition and Trade     Hybrid Journal   (Followers: 8, SJR: 0.327, h-index: 15)
J. of Infection and Chemotherapy     Hybrid Journal   (Followers: 2, SJR: 0.673, h-index: 46)
J. of Information Technology     Hybrid Journal   (Followers: 54, SJR: 1.474, h-index: 55)
J. of Information Technology Teaching Cases     Hybrid Journal   (Followers: 10)
J. of Infrared, Millimeter and Terahertz Waves     Hybrid Journal   (Followers: 2, SJR: 1.25, h-index: 36)
J. of Inherited Metabolic Disease     Hybrid Journal   (Followers: 2, SJR: 1.389, h-index: 77)
J. of Inorganic and Organometallic Polymers and Materials     Partially Free   (Followers: 10, SJR: 0.338, h-index: 33)
J. of Insect Behavior     Hybrid Journal   (Followers: 7, SJR: 0.569, h-index: 39)
J. of Insect Conservation     Hybrid Journal   (Followers: 10, SJR: 0.872, h-index: 43)
J. of Intelligent and Robotic Systems     Hybrid Journal   (Followers: 3, SJR: 0.629, h-index: 43)
J. of Intelligent Information Systems     Hybrid Journal   (Followers: 1, SJR: 0.691, h-index: 43)
J. of Intelligent Manufacturing     Hybrid Journal   (Followers: 3, SJR: 1.397, h-index: 54)
J. of Interventional Cardiac Electrophysiology     Hybrid Journal   (SJR: 0.93, h-index: 43)
J. of Intl. Business Studies     Hybrid Journal   (Followers: 37, SJR: 4.208, h-index: 130)
J. of Intl. Entrepreneurship     Hybrid Journal   (Followers: 12, SJR: 0.549, h-index: 23)
J. of Intl. Migration and Integration / Revue de l integration et de la migration internationale     Hybrid Journal   (Followers: 14, SJR: 0.308, h-index: 13)
J. of Intl. Relations and Development     Hybrid Journal   (Followers: 20, SJR: 0.793, h-index: 22)
J. of Labor Research     Hybrid Journal   (Followers: 19, SJR: 0.394, h-index: 27)
J. of Logic, Language and Information     Hybrid Journal   (Followers: 6, SJR: 0.288, h-index: 25)
J. of Low Temperature Physics     Hybrid Journal   (Followers: 3, SJR: 0.531, h-index: 52)
J. of Machinery Manufacture and Reliability     Hybrid Journal   (Followers: 2, SJR: 0.203, h-index: 7)
J. of Mammalian Evolution     Hybrid Journal   (Followers: 6, SJR: 1.134, h-index: 37)
J. of Mammary Gland Biology and Neoplasia     Hybrid Journal   (Followers: 2, SJR: 2.252, h-index: 83)
J. of Management and Governance     Hybrid Journal   (Followers: 9, SJR: 0.805, h-index: 33)
J. of Management Control     Hybrid Journal   (Followers: 5, SJR: 0.605, h-index: 6)
J. of Marine Science and Application     Hybrid Journal   (Followers: 2, SJR: 0.439, h-index: 11)
J. of Marine Science and Technology     Hybrid Journal   (Followers: 3, SJR: 0.235, h-index: 19)
J. of Maritime Archaeology     Hybrid Journal   (Followers: 17, SJR: 0.228, h-index: 8)
J. of Market-Focused Management     Hybrid Journal   (Followers: 2)
J. of Marketing Analytics     Hybrid Journal   (Followers: 5)
J. of Material Cycles and Waste Management     Hybrid Journal   (Followers: 2, SJR: 0.449, h-index: 22)
J. of Materials Engineering and Performance     Hybrid Journal   (Followers: 23, SJR: 0.544, h-index: 40)
J. of Materials Science     Hybrid Journal   (Followers: 22, SJR: 0.836, h-index: 123)
J. of Materials Science : Materials in Electronics     Hybrid Journal   (Followers: 4)
J. of Materials Science : Materials in Medicine     Hybrid Journal   (Followers: 4)
J. of Mathematical Biology     Hybrid Journal   (Followers: 9, SJR: 1.011, h-index: 71)
J. of Mathematical Chemistry     Hybrid Journal   (Followers: 3, SJR: 0.497, h-index: 45)
J. of Mathematical Fluid Mechanics     Hybrid Journal   (Followers: 8, SJR: 1.22, h-index: 22)
J. of Mathematical Imaging and Vision     Hybrid Journal   (Followers: 5, SJR: 0.901, h-index: 53)
J. of Mathematical Modelling and Algorithms     Hybrid Journal   (Followers: 1, SJR: 0.414, h-index: 23)
J. of Mathematical Sciences     Hybrid Journal   (SJR: 0.272, h-index: 23)
J. of Mathematics Teacher Education     Hybrid Journal   (Followers: 16, SJR: 1.062, h-index: 20)
J. of Maxillofacial and Oral Surgery     Hybrid Journal   (Followers: 3)
J. of Mechanical Science and Technology     Hybrid Journal   (Followers: 5, SJR: 0.589, h-index: 26)
J. of Medical and Biological Engineering     Hybrid Journal   (Followers: 3, SJR: 0.387, h-index: 18)
J. of Medical Humanities     Hybrid Journal   (Followers: 22, SJR: 0.299, h-index: 18)
J. of Medical Systems     Hybrid Journal   (SJR: 0.717, h-index: 44)
J. of Medical Toxicology     Hybrid Journal   (Followers: 5, SJR: 0.874, h-index: 28)
J. of Medical Ultrasonics     Hybrid Journal   (Followers: 2, SJR: 0.18, h-index: 13)
J. of Medicine and the Person     Hybrid Journal  
J. of Membrane Biology     Hybrid Journal   (Followers: 1, SJR: 0.738, h-index: 82)
J. of Micro-Bio Robotics     Hybrid Journal   (SJR: 0.28, h-index: 3)
J. of Microbiology     Hybrid Journal   (Followers: 8, SJR: 0.741, h-index: 43)
J. of Mining Science     Hybrid Journal   (Followers: 4, SJR: 0.317, h-index: 16)
J. of Molecular Evolution     Hybrid Journal   (Followers: 6, SJR: 0.952, h-index: 108)
J. of Molecular Histology     Hybrid Journal   (Followers: 4, SJR: 0.755, h-index: 48)
J. of Molecular Medicine     Hybrid Journal   (Followers: 11, SJR: 2.165, h-index: 113)
J. of Molecular Modeling     Hybrid Journal   (Followers: 4, SJR: 0.466, h-index: 50)
J. of Molecular Neuroscience     Partially Free   (Followers: 11, SJR: 0.988, h-index: 69)
J. of Mountain Science     Hybrid Journal   (Followers: 2, SJR: 0.418, h-index: 15)
J. of Muscle Research and Cell Motility     Hybrid Journal   (Followers: 1, SJR: 1.264, h-index: 55)
J. of Nanoparticle Research     Hybrid Journal   (Followers: 3, SJR: 0.583, h-index: 84)
J. of Natural Medicines     Hybrid Journal   (SJR: 0.602, h-index: 28)
J. of Near-Death Studies     Hybrid Journal   (Followers: 1)
J. of Nephrology     Hybrid Journal   (Followers: 4, SJR: 0.689, h-index: 55)
J. of Network and Systems Management     Hybrid Journal   (SJR: 0.466, h-index: 26)
J. of Neural Transmission     Hybrid Journal   (Followers: 2, SJR: 1.034, h-index: 86)
J. of Neuro-Oncology     Hybrid Journal   (Followers: 2, SJR: 1.274, h-index: 90)
J. of Neuroimmune Pharmacology     Hybrid Journal   (Followers: 1, SJR: 1.662, h-index: 45)
J. of Neurology     Hybrid Journal   (Followers: 16, SJR: 1.429, h-index: 105)
J. of NeuroVirology     Hybrid Journal   (Followers: 1, SJR: 0.979, h-index: 69)
J. of Nondestructive Evaluation     Hybrid Journal   (Followers: 9, SJR: 0.863, h-index: 27)
J. of Nonlinear Science     Hybrid Journal   (SJR: 1.887, h-index: 42)
J. of Nonverbal Behavior     Hybrid Journal   (Followers: 5, SJR: 0.723, h-index: 47)
J. of Nuclear Cardiology     Hybrid Journal   (SJR: 1.024, h-index: 68)
J. of Nutrition, Health and Aging     Hybrid Journal   (Followers: 24, SJR: 0.919, h-index: 60)
J. of Obstetrics and Gynecology of India     Hybrid Journal   (Followers: 4, SJR: 0.214, h-index: 6)
J. of Occupational Rehabilitation     Hybrid Journal   (Followers: 16, SJR: 0.811, h-index: 51)
J. of Ocean Engineering and Marine Energy     Hybrid Journal   (Followers: 3)
J. of Ocean University of China (English Edition)     Hybrid Journal   (Followers: 1, SJR: 0.237, h-index: 11)
J. of Oceanography     Hybrid Journal   (Followers: 10, SJR: 0.796, h-index: 52)
J. of Ocular Biology, Diseases, and Informatics     Hybrid Journal   (SJR: 0.183, h-index: 11)
J. of Optical and Fiber Communications Reports     Hybrid Journal   (Followers: 4)
J. of Optics     Hybrid Journal   (Followers: 8, SJR: 0.214, h-index: 8)
J. of Optimization Theory and Applications     Hybrid Journal   (Followers: 5, SJR: 0.898, h-index: 65)
J. of Ornithology     Hybrid Journal   (Followers: 23)
J. of Orofacial Orthopedics / Fortschritte der Kieferorthopädie     Hybrid Journal   (SJR: 0.574, h-index: 33)
J. of Orthopaedic Science     Hybrid Journal   (Followers: 4, SJR: 0.708, h-index: 48)
J. of Paleolimnology     Hybrid Journal   (Followers: 5, SJR: 0.984, h-index: 64)

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Journal Cover Evolutionary Intelligence
  [SJR: 0.947]   [H-I: 14]   [1 followers]  Follow
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 1864-5917 - ISSN (Online) 1864-5909
   Published by Springer-Verlag Homepage  [2351 journals]
  • Multilevel image thresholding using entropy of histogram and recently
           developed population-based metaheuristic algorithms
    • Authors: Seyed Jalaleddin Mousavirad; Hossein Ebrahimpour-Komleh
      Abstract: Multilevel thresholding is one of the most broadly used approaches to image segmentation. However, the traditional techniques of multilevel thresholding are time-consuming, especially when the number of the threshold values is high. Thus, population-based metaheuristic (P-metaheuristic) algorithms can be used to overcome this limitation. P-metaheuristic algorithms are a type of optimization algorithms, which improve a set of solutions using an iterative process. For this purpose, image thresholding problem should be seen as an optimization problem. This paper proposes multilevel image thresholding for image segmentation using several recently presented P-metaheuristic algorithms, including whale optimization algorithm, grey wolf optimizer, cuckoo optimization algorithm, biogeography-based optimization, teaching–learning-based optimization, gravitational search algorithm, imperialist competitive algorithm, and cuckoo search. Kapur’s entropy is used as the objective function. To conduct a more comprehensive comparison, the mentioned P-metaheuristic algorithms were compared with five others. Several experiments were conducted on 12 benchmark images to compare the algorithms regarding objective function value, peak signal to noise ratio (PSNR), feature similarity index (FSIM), structural similarity index (SSIM), and stability. In addition, Friedman test and Wilcoxon signed rank test were carried out as the nonparametric statistical methods to compare P-metaheuristic algorithms. Eventually, to create a more reliable result, another objective function was evaluated based on Cross Entropy.
      PubDate: 2017-06-23
      DOI: 10.1007/s12065-017-0152-y
  • A participatory search algorithm
    • Authors: Yi Ling Liu; Fernando Gomide
      Abstract: Search is one of the most useful procedures employed in numerous situations such as optimization, machine learning, information processing and retrieval. This paper introduces participatory search, a population-based heuristic search algorithm based on the participatory learning paradigm. Participatory search is an algorithm in which search progresses forming pools of compatible individuals, keeping the one that is the most compatible with the current best individual in the population, and introducing random individuals in each algorithm step. Recombination is a convex combination modulated by the compatibility between individuals while mutation is an instance of differential variation modulated by compatibility between selected and recombined individuals. The nature of the recombination and mutation operators are studied, and the convergence analysis of the algorithm is pursued within the framework of random search theory. The algorithm is evaluated using ten benchmark real-valued optimization problems and its performance is compared against population-based optimization algorithms representative of the current state of the art. The participatory search algorithm is also evaluated using a suite of twenty eight benchmark functions of a recent evolutionary, real-valued optimization competition, to compare its performance against the competition winners. Computational results suggest that participatory search algorithm performs best amongst the algorithms addressed in this paper.
      PubDate: 2017-03-18
      DOI: 10.1007/s12065-016-0151-4
  • Feature selection for speaker verification using genetic programming
    • Authors: Róisín Loughran; Alexandros Agapitos; Ahmed Kattan; Anthony Brabazon; Michael O’Neill
      Abstract: We present a study examining feature selection from high performing models evolved using genetic programming (GP) on the problem of automatic speaker verification (ASV). ASV is a highly unbalanced binary classification problem in which a given speaker must be verified against everyone else. We evolve classification models for 10 individual speakers using a variety of fitness functions and data sampling techniques and examine the generalisation of each model on a 1:9 unbalanced set. A significant difference between train and test performance is found which may indicate overfitting in the models. Using only the best generalising models, we examine two methods for selecting the most important features. We compare the performance of a number of tuned machine learning classifiers using the full 275 features and a reduced set of 20 features from both feature selection methods. Results show that using only the top 20 features found in high performing GP programs led to test classifications that are as good as, or better than, those obtained using all data in the majority of experiments undertaken. The classification accuracy between speakers varies considerably across all experiments showing that some speakers are easier to classify than others. This indicates that in such real-world classification problems, the content and quality of the original data has a very high influence on the quality of results obtainable.
      PubDate: 2017-01-11
      DOI: 10.1007/s12065-016-0150-5
  • Mutual information for feature selection: estimation or counting?
    • Authors: Hoai Bach Nguyen; Bing Xue; Peter Andreae
      Pages: 95 - 110
      Abstract: In classification, feature selection is an important pre-processing step to simplify the dataset and improve the data representation quality, which makes classifiers become better, easier to train, and understand. Because of an ability to analyse non-linear interactions between features, mutual information has been widely applied to feature selection. Along with counting approaches, a traditional way to calculate mutual information, many mutual information estimations have been proposed to allow mutual information to work directly on continuous datasets. This work focuses on comparing the effect of counting approach and kernel density estimation (KDE) approach in feature selection using particle swarm optimisation as a search mechanism. The experimental results on 15 different datasets show that KDE can work well on both continuous and discrete datasets. In addition, feature subsets evolved by KDE achieves similar or better classification performance than the counting approach. Furthermore, the results on artificial datasets with various interactions show that KDE is able to capture correctly the interaction between features, in both relevance and redundancy, which can not be achieved by using the counting approach.
      PubDate: 2016-08-20
      DOI: 10.1007/s12065-016-0143-4
      Issue No: Vol. 9, No. 3 (2016)
  • A new bats echolocation-based algorithm for single objective optimisation
    • Authors: Nafrizuan Mat Yahya; M. Osman Tokhi; Hyreil Anuar Kasdirin
      Pages: 1 - 20
      Abstract: Bats sonar algorithm (BSA) as a swarm intelligence approach utilises the concept of echolocation of bats to find prey. However, the algorithm is unable to achieve good precision and fast convergence rate to the optimum solution. With this in mind, an adaptive bats sonar algorithm is introduced with new paradigms of real bats echolocation behaviour. The performance of the algorithm is validated through rigorous tests with several single objective optimisation benchmark test functions. The obtained results show that the proposed scheme outperforms the BSA in terms of accuracy and convergence speed and can be efficiently employed to solve engineering problems.
      PubDate: 2016-02-18
      DOI: 10.1007/s12065-016-0134-5
      Issue No: Vol. 9, No. 1-2 (2016)
  • MCOA: mutated and self-adaptive cuckoo optimization algorithm
    • Authors: Seyed Alireza Mohseni; Tony Wong; Vincent Duchaine
      Pages: 21 - 36
      Abstract: As with other nature-inspired algorithms, the cuckoo optimization algorithm (COA) produces a population of candidate solutions to find the (near-) optimal solutions to a problem. In this paper, several modifications, including a dynamic mutation operator, are proposed for this algorithm. Design of experiments is employed to determine factors controlling the value of parameters and the target levels of those values to achieve desirable output. The efficiency of the modified COA algorithm is substantiated with the help of several optimization test problems. The results are then compared to other well-known algorithms such as PSO, DE and harmony search using a non-parametric statistical procedure. In order to analyze its effectiveness, the proposed modified COA is applied to a feature selection problem and spacecraft attitude control problem.
      PubDate: 2016-04-12
      DOI: 10.1007/s12065-016-0135-4
      Issue No: Vol. 9, No. 1-2 (2016)
  • An upper and lower CUSUM for signal normalization in the dendritic cell
    • Authors: Mohamad Farhan Mohamad Mohsin; Abdul Razak Hamdan; Azuraliza Abu Bakar
      Pages: 37 - 51
      Abstract: Signal normalization is a part of signal formalization which is a vital data pre-processing constraint required for the functioning of the dendritic cell algorithm. In existing applications, most normalization algorithms are developed purposely for a specific application with drawing on human domain expertise and very few algorithms are designed for general problems. This makes it difficult for the inexperienced user to exploit existing approaches to another problem, particularly when the initial information about the problem is limited. Therefore, this study proposes a new signal normalization method for the dendritic cell algorithm that uses the statistical upper and lower cumulative sum so that the algorithm can be applied to general classification problems. In addition, a new method to calculate the anomaly threshold based on the average mature-contact antigen value is presented to suit the proposed algorithm. The proposed model is evaluated by applying it to eight universal classification datasets and assessing its performance according to four measurement metrics: detection rate, specificity, false alarm rate, and accuracy. Its performance is compared with that of the existing dendritic cell algorithm and four non-bio-inspired classifiers, namely, rough set, decision tree, naïve Bayes, and multilayer perceptron. The results show that the proposed model outperforms the existing model and the other classifiers as well as demonstrates a significant improvement in terms of specificity, false alarm rate, and accuracy for all datasets. This indicates that the proposed normalization approach can be applied to general classification problems and can improve detection performance.
      PubDate: 2016-04-20
      DOI: 10.1007/s12065-016-0136-3
      Issue No: Vol. 9, No. 1-2 (2016)
  • Characterising order book evolution using self-organising maps
    • Authors: Anthony Brabazon; Piotr Lipinski; Philip Hamill
      Abstract: Trading on major financial markets is typically conducted via electronic order books whose state is visible to market participants in real-time. A significant research literature has emerged concerning order book evolution, focussing on characteristics of the order book such as the time series of trade prices, movements in the bid-ask spread and changes in the depth of the order book at each price point. The latter two items can be characterised as order book shape where the book is viewed as a histogram with the size of the bar at each price point corresponding to the volume of shares demanded or offered for sale at that price. Order book shape is of interest to market participants as it provides insight as to current, and potentially future, market liquidity. Questions such as what shapes are commonly observed in order books and whether order books transition between certain shape patterns over time are of evident interest from both a theoretical and practical standpoint. In this study, using high-frequency equity data from the London Stock Exchange, we apply an unsupervised clustering methodology to determine clusters of common order book shapes, and also attempt to assess the transition probabilities between these clusters. Findings indicate that order books for individual stocks display intraday seasonality, exhibit some common patterns, and that transitions between order book patterns over sequential time periods is not random.
      PubDate: 2016-11-17
      DOI: 10.1007/s12065-016-0149-y
  • Foreword: special issue on computational finance and economics
    • Authors: Anthony Brabazon; Michael Kampouridis
      PubDate: 2016-11-16
      DOI: 10.1007/s12065-016-0148-z
  • Enhanced multiobjective population-based incremental learning with
           applications in risk treaty optimization
    • Authors: Omar Andres Carmona Cortes; Andrew Rau-Chaplin
      Abstract: The purpose of this paper is to revisit the Multiobjective Population-Based Incremental Learning method and show how its performance can be improved in the context of a real-world financial optimization problem . The proposed enhancements lead to both better performance and improvements in the quality of solutions, which can represent millions of dollars for the insurance company in terms of recoveries. Its performance was assessed in terms of runtime and speedup when parallelized. Also, metrics such as the average number of solutions, the average hypervolume, and coverage have been used in order to compare the Pareto frontiers obtained by both the original and enhanced methods. Results indicated that the proposed method is 22.1% faster, present more solutions in the average (better defining the Pareto frontier) and often generates solutions having larger hypervolumes. The method achieves a speedup of 15.7 on 16 cores of a dual socket Intel multi-core machine when solving a Reinsurance Contract Optimization problem involving 15 layers or sub-contracts .
      PubDate: 2016-10-24
      DOI: 10.1007/s12065-016-0147-0
  • Towards a new Praxis in optinformatics targeting knowledge re-use in
           evolutionary computation: simultaneous problem learning and optimization
    • Authors: D. Lim; Y. S. Ong; A. Gupta; C. K. Goh; P. S. Dutta
      Abstract: As the field of evolutionary optimization continues to expand, it is becoming increasingly common to incorporate various machine learning approaches, such as clustering, classification, and regression models, to improve algorithmic efficiency. However, we note that although problem learning is popularly used in improving the ongoing optimization process, little effort is ever made in extracting re-usable domain knowledge. In other words, the acquired knowledge is seldom transferred and exploited for future design exercises. Focusing on evolutionary optimization, in this paper we investigate the concept of simultaneous problem learning and optimization inspired by the following notions: (1) that prior/dynamically acquired knowledge can enhance the effectiveness of evolutionary search, and (2) that evolution can be geared towards gathering crucial knowledge about the underlying problem. Taking benchmark functions as well as an engineering (process) design problem into consideration, we demonstrate the efficacy of a novel classifier-assisted constrained EA towards simultaneous evolutionary search and problem learning.
      PubDate: 2016-10-11
      DOI: 10.1007/s12065-016-0146-1
  • Selecting and estimating interest rate models with evolutionary methods
    • Authors: Dietmar Maringer; Sebastian H. M. Deininger
      Abstract: Selecting and estimating parsimonious models is often desired, but hard to achieve. This is particularly true when models can potentially contain a very large number of parameters but data are scarce—as is the case for many macro-economic models in general and interest-rate models in particular. These models need to cater for a large number of potential relationships and dependencies, but are fitted on low-frequency data to focus on the bigger picture and long-term effects. To identify the ideal model and estimating it is then particularly demanding from an optimization perspective. In this paper, we suggest an evolutionary approach that considers model selection and estimation simultaneously. Numerical experiments with artificial data suggest that the approach is well suited for this type of problem. In an empirical application for short-term and long-term interest rates denominated in US dollar, euro and the Japanese yen, respectively, parsimonious model structures are identified that highlight the dependencies as well as spill-overs across maturities and currencies.
      PubDate: 2016-09-27
      DOI: 10.1007/s12065-016-0145-2
  • Anatomy of a portfolio optimizer under a limited budget constraint
    • Authors: Igor Deplano; Giovanni Squillero; Alberto Tonda
      Abstract: Predicting the market’s behavior to profit from trading stocks is far from trivial. Such a task becomes even harder when investors do not have large amounts of money available, and thus cannot influence this complex system in any way. Machine learning paradigms have been already applied to financial forecasting, but usually with no restrictions on the size of the investor’s budget. In this paper, we analyze an evolutionary portfolio optimizer for the management of limited budgets, dissecting each part of the framework, discussing in detail the issues and the motivations that led to the final choices. Expected returns are modeled resorting to artificial neural networks trained on past market data, and the portfolio composition is chosen by approximating the solution to a multi-objective constrained problem. An investment simulator is eventually used to measure the portfolio performance. The proposed approach is tested on real-world data from New York’s, Milan’s and Paris’ stock exchanges, exploiting data from June 2011 to May 2014 to train the framework, and data from June 2014 to July 2015 to validate it. Experimental results demonstrate that the presented tool is able to obtain a more than satisfying profit for the considered time frame.
      PubDate: 2016-09-02
      DOI: 10.1007/s12065-016-0144-3
  • Foreword: special issue on evolutionary computer vision and pattern
    • Authors: Stefano Cagnoni; Mengjie Zhang
      PubDate: 2016-08-18
      DOI: 10.1007/s12065-016-0142-5
  • Improving performance for classification with incomplete data using
           wrapper-based feature selection
    • Authors: Cao Truong Tran; Mengjie Zhang; Peter Andreae; Bing Xue
      Abstract: Missing values are an unavoidable problem of many real-world datasets. Inadequate treatment of missing values may result in large errors on classification; thus, dealing well with missing values is essential for classification. Feature selection has been well known for improving classification, but it has been seldom used for improving classification with incomplete datasets. Moreover, some classifiers such as C4.5 are able to directly classify incomplete datasets, but they often generate more complex classifiers with larger classification errors. The purpose of this paper is to propose a wrapper-based feature selection method to improve the ability of a classifier able to classify incomplete datasets. In order to achieve the purpose, the feature selection method evaluates feature subsets using a classifier able to classify incomplete datasets. Empirical results on 14 datasets using particle swarm optimisation for searching feature subsets and C4.5 for evaluating the feature subsets in the feature selection method show that the wrapper-based feature selection is not only able to improve classification accuracy of the classifier, but also able to reduce the size of trees generated by the classifier.
      PubDate: 2016-08-09
      DOI: 10.1007/s12065-016-0141-6
  • Reduced projection angles for binary tomography with particle aggregation
    • Authors: Mohammad Majid al-Rifaie; Tim Blackwell
      Abstract: This paper extends particle aggregate reconstruction technique (PART), a reconstruction algorithm for binary tomography based on the movement of particles. PART supposes that pixel values are particles, and that particles diffuse through the image, staying together in regions of uniform pixel value known as aggregates. In this work, a variation of this algorithm is proposed and a focus is placed on reducing the number of projections and whether this impacts the reconstruction of images. The algorithm is tested on three phantoms of varying sizes and numbers of forward projections and compared to filtered back projection, a random search algorithm and to SART, a standard algebraic reconstruction method. It is shown that the proposed algorithm outperforms the aforementioned algorithms on small numbers of projections. This potentially makes the algorithm attractive in scenarios where collecting less projection data are inevitable.
      PubDate: 2016-08-08
      DOI: 10.1007/s12065-016-0140-7
  • Population based ant colony optimization for reconstructing ECG signals
    • Authors: Yih-Chun Cheng; Tom Hartmann; Pei-Yun Tsai; Martin Middendorf
      Abstract: A population based ant colony optimization algorithm (PACO) for the reconstruction of electrocardiogram (ECG) signals is proposed. Specifically, the PACO finds a subset of nonzero positions of a sparse wavelet domain ECG signal vector that is used for the reconstruction of the signal. A time window is used by the proposed PACO for fixing certain decisions of the ants during the run of the algorithm. The optimization behaviour of the PACO is compared with various algorithms from the literature for ECG signal reconstruction, and with two random search heuristics. Experimental results are presented for ECG signals from the MIT-BIT Arrhythmia database. The influence of several algorithmic parameters and of a local search procedure is evaluated. The results show that the proposed PACO algorithm reconstructs ECG signals with high accuracy.
      PubDate: 2016-07-30
      DOI: 10.1007/s12065-016-0139-0
  • On the complexity of the El Farol Bar game: a sensitivity analysis
    • Authors: Shu-Heng Chen; Umberto Gostoli
      Abstract: In this paper, we carry out a sensitivity analysis for an agent-based model of the use of public resources as manifested by the El Farol Bar problem. An early study using the same model has shown that a good-society equilibrium, characterized by both economic efficiency and economic equality, can be achieved probabilistically by a von Neumann network, and can be achieved surely with the presence of some agents having social preferences, such as the inequity-averse preference or the ‘keeping-up-with-the-Joneses’ preference. In this study, we examine this fundamental result by exploring the inherent complexity of the model; specifically, we address the effect of the three key parameters related to size, namely, the network size, the neighborhood size, and the memory size. We find that social preferences still play an important role over all the sizes considered. Nonetheless, it is also found that when network size becomes large, the parameter, the bar capacity (the attendance threshold), may also play a determining role.
      PubDate: 2016-07-02
      DOI: 10.1007/s12065-016-0138-1
  • Evolving goal-driven multi-agent communication: what, when, and to whom
    • Authors: Alhanoof Althnian; Arvin Agah
      Abstract: This paper presents an evolutionary approach that, given a performance goal, produces a communication strategy that can improve a multi-agent system’s performance with respect to the desired goal. The evolved strategy determines what, when, and to whom agents communicate. The proposed approach further enables tuning the trade-off between the performance goal and communication cost, to produce a strategy that achieves a good balance between the two objectives, according the system’s designer needs. Experiments are designed to evaluate the approach using the Wumpus World application domain, with variations of three factors: fitness parameters (including objectives’ weights and action and communication costs), fitness goal, and simulation environment. Results show that the system’s performance can be highly tuned by controlling communication, and that the presented approach has significant utilization in improving the performance with respect to the goal.
      PubDate: 2016-06-13
      DOI: 10.1007/s12065-016-0137-2
  • Quadratic assignment problem: a landscape analysis
    • Authors: Mohammad-H. Tayarani-N.; Adam Prügel-Bennett
      Pages: 165 - 184
      Abstract: The anatomy of the fitness landscape for the quadratic assignment problem is studied in this paper. We study the properties of both random problems, and real-world problems. Using auto-correlation as a measure for the landscape ruggedness, we study the landscape of the problems and show how this property is related to the problem matrices with which the problems are represented. Our main goal in this paper is to study new properties of the fitness landscape, which have not been studied before, and we believe are more capable of reflecting the problem difficulties. Using local search algorithm which exhaustively explore the plateaus and the local optima, we explore the landscape, store all the local optima we find, and study their properties. The properties we study include the time it takes for a local search algorithm to find local optima, the number of local optima, the probability of reaching the global optimum, the expected cost of the local optima around the global optimum and the basin of attraction of the global and local optima. We study the properties for problems of different sizes, and through extrapolations, we show how the properties change with the system size and why the problem becomes harder as the system size grows. In our study we show how the real-world problems are similar to, or different from the random problems. We also try to show what properties of the problem matrices make the landscape of the real problems be different from or similar to one another.
      PubDate: 2015-05-22
      DOI: 10.1007/s12065-015-0132-z
      Issue No: Vol. 8, No. 4 (2015)
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