Publications


Total IF: 23.045

H index: 7 (see a graph)

Number of citations: 151 (see a list)

2019
  • K. Michalak
    Low-Dimensional Euclidean Embedding for Visualization of Search Spaces in Combinatorial Optimization
    IEEE Transactions on Evolutionary Computation (IF2017 = 8.124), ISSN: 1089-778X, 23(2), pp. 232-246, IEEE, 2019, DOI: 10.1109/TEVC.2018.2846636.
    [BibTex]   [PDF]

    The original publication is available at IEEE

  • K. Michalak
    Surrogate-based Optimization for Reduction of Contagion Susceptibility in Financial Systems
    GECCO '19 Proceedings of the Genetic and Evolutionary Computation Conference, Prague, Czech Republic - July 13-17, 2019, ISBN: 978-1-4503-5618-3, pp. 1266-1274, ACM New York, NY, USA, 2019.
    [BibTex]   [PDF]   [Presentation]

    The original publication is available at ACM

  • K. Michalak
    Evolutionary optimization of epidemic control strategies for livestock disease prevention
    GECCO '19 Proceedings of the Genetic and Evolutionary Computation Conference Companion, Prague, Czech Republic - July 13-17, 2019, ISBN: 978-1-4503-6748-6, pp. 389-390, ACM New York, NY, USA, 2019.
    [BibTex]   [PDF]   [Poster]

    The original publication is available at ACM

  • K. Michalak
    Solving the Parameterless Firefighter Problem using Multiobjective Evolutionary Algorithms
    GECCO '19 Proceedings of the Genetic and Evolutionary Computation Conference Companion, Prague, Czech Republic - July 13-17, 2019, ISBN: 978-1-4503-6748-6, pp. 1321-1328, ACM New York, NY, USA, 2019.
    [BibTex]   [PDF]   [Presentation]

    The original publication is available at ACM

  • K. Michalak
    Low-Dimensional Euclidean Embedding for Visualization of Search Spaces in Combinatorial Optimization [Hot-off the Press]
    GECCO '19 Proceedings of the Genetic and Evolutionary Computation Conference Companion, Prague, Czech Republic - July 13-17, 2019, ISBN: 978-1-4503-6748-6, pp. 27-28, ACM New York, NY, USA, 2019.
    [BibTex]   [PDF]   [Presentation]

    The original publication is available at ACM

  • P. Lipinski, K. Michalak
    Deriving knowledge from local optima networks for evolutionary optimization in inventory routing problem
    GECCO '19 Proceedings of the Genetic and Evolutionary Computation Conference Companion, Prague, Czech Republic - July 13-17, 2019, ISBN: 978-1-4503-6748-6, pp. 1551-1558, ACM New York, NY, USA, 2019.
    [BibTex]   [PDF]   [Presentation]

    The original publication is available at ACM

  • P. Lipinski, K. Michalak
    Multidimensional Time Series Feature Engineering by Hybrid Evolutionary Approach
    GECCO '19 Proceedings of the Genetic and Evolutionary Computation Conference Companion, Prague, Czech Republic - July 13-17, 2019, ISBN: 978-1-4503-6748-6, pp. 67-68, ACM New York, NY, USA, 2019.
    [BibTex]   [PDF]   [Poster]

    The original publication is available at ACM

2018
  • K. Michalak
    Informed Mutation Operator using Machine Learning for Optimization in Epidemics Prevention
    GECCO '18 Proceedings of the Genetic and Evolutionary Computation Conference, Kyoto, Japan - July 15-19, 2018, ISBN: 978-1-4503-5618-3, pp. 1294-1301, ACM New York, NY, USA, 2018.
    [BibTex]   [PDF]   [Presentation]

    The original publication is available at ACM

  • K. Michalak
    ED-LS - A Heuristic Local Search for the Multiobjective Firefighter Problem [Hot-off the Press]
    GECCO'18 Proceedings of the Genetic and Evolutionary Computation Conference Companion, Kyoto, Japan - July 15-19, 2018, ISBN: 978-1-4503-5764-7, pp. 25-26, ACM New York, NY, USA, 2018.
    [BibTex]   [PDF]   [Presentation]

    The original publication is available at ACM

  • P. Lipinski, K. Michalak
    An Evolutionary Algorithm with Practitioner's-Knowledge-Based Operators for the Inventory Routing Problem
    Evolutionary Computation in Combinatorial Optimization 18th European Conference, EvoCOP 2018, Parma, Italy, April 4-6, 2018, Proceedings, Lecture Notes in Computer Science, volume 10782, ISBN: 978-3-319-77448-0, pp. 146-157, Springer, 2018.

  • K. Michalak
    Knowledge-based Solution Construction for Evolutionary Minimization of Systemic Risk
    The 19th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2018, Madrid, Spain, 21-23 November, 2018, Proceedings, Lecture Notes in Computer Science, volume 11314, ISBN: 978-3-030-03492-4, pp. 58-68, Springer, 2018.
    [BibTex]   [PDF]   [Presentation]

    The original publication is available at www.springerlink.com

  • K. Michalak
    Crossover Operator using Knowledge Transfer for the Firefighter Problem
    The 19th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2018, Madrid, Spain, 21-23 November, 2018, Proceedings, Lecture Notes in Computer Science, volume 11314, ISBN: 978-3-030-03492-4, pp. 305-316, Springer, 2018.
    [BibTex]   [PDF]   [Presentation]

    The original publication is available at www.springerlink.com

2017
  • K. Michalak
    ED-LS - A Heuristic Local Search for the Multiobjective Firefighter Problem
    Applied Soft Computing (IF2017 = 3.907), volume 59, pp. 389-404, Elsevier, 2017.
    [BibTex]   [PDF]

    The original publication is available at Elsevier

  • K. Michalak
    Simulation-based Crossover for the Firefighter Problem
    GECCO'17 Proceedings of the Genetic and Evolutionary Computation Conference, Berlin, Germany - July 15-19, 2017, pp. 601-608, ACM New York, NY, USA, 2017.
    [BibTex]   [PDF]   [Presentation]

    The original publication is available at ACM

  • K. Michalak
    Evolutionary algorithm with a directional local search for multiobjective optimization in combinatorial problems [Hot-off the Press]
    GECCO'17 Proceedings of the Genetic and Evolutionary Computation Conference Companion, Berlin, Germany - July 15-19, 2017, pp. 7-8, ACM New York, NY, USA, 2017.
    [BibTex]   [PDF]   [Presentation]

    The original publication is available at ACM

  • K. Michalak
    The MOEA/D algorithm with gaussian neighbourhoods for the multiobjective travelling salesman problem
    GECCO'17 Proceedings of the Genetic and Evolutionary Computation Conference Companion, Berlin, Germany - July 15-19, 2017, pp. 155-156, ACM New York, NY, USA, 2017.
    [BibTex]   [PDF]   [Poster]

    The original publication is available at ACM

  • K. Michalak
    Reducing systemic risk in multiplex networks using evolutionary optimization
    GECCO'17 Proceedings of the Genetic and Evolutionary Computation Conference Companion, Berlin, Germany - July 15-19, 2017, pp. 289-290, ACM New York, NY, USA, 2017.
    [BibTex]   [PDF]   [Poster]

    The original publication is available at ACM

  • K. Michalak
    Estimation of Distribution Algorithms for the Firefighter Problem
    Evolutionary Computation in Combinatorial Optimization 17th European Conference, EvoCOP 2017, Amsterdam, The Netherlands, April 19-21, 2017, Proceedings, Lecture Notes in Computer Science, volume 10197, pp. 108-123, Springer, 2017.
    [BibTex]   [PDF]   [Presentation]

    The original publication is available at www.springerlink.com

2016
  • K. Michalak, A. Lancucki, P. Lipinski
    Multiobjective optimization of frequent pattern models in ultra-high frequency time series: Stability versus universality
    2016 IEEE Congress on Evolutionary Computation (CEC), pp. 3491-3498, IEEE, 2016.
    [BibTex]
    Cited by: [34,70]

  • P. Lipinski, K. Michalak, A. Lancucki
    Improving Classification of Patterns in Ultra-High Frequency Time Series with Evolutionary Algorithms
    GECCO '16 Proceedings of the 2016 Genetic and Evolutionary Computation Conference Companion, pp. 127-128, ACM, 2016.
    [BibTex]
    Cited by: [34,70]

  • K. Michalak, J. D. Knowles
    Simheuristics for the Multiobjective Nondeterministic Firefighter Problem in a Time-Constrained Setting
    Applications of Evolutionary Computation 19th European Conference, EvoApplications 2016, Porto, Portugal, March 30 - April 1, 2016, Proceedings, Part II, Lecture Notes in Computer Science, volume 9598, pp. 248-265, Springer, 2016.
    [BibTex]   [PDF]   [Presentation]   [Poster]

    The original publication is available at www.springerlink.com

  • K. Michalak
    Sim-EDA: A Multipopulation Estimation of Distribution Algorithm Based on Problem Similarity
    Evolutionary Computation in Combinatorial Optimization: 16th European Conference, EvoCOP 2016, Porto, Portugal, March 30 - April 1, 2016, Proceedings, Lecture Notes in Computer Science, volume 9595, pp. 235-250, Springer, 2016.
    [BibTex]   [PDF]   [Presentation]   [Poster]

    The original publication is available at www.springerlink.com
    Cited by: [50]

  • K. Michalak
    Evolutionary algorithm with a directional local search for multiobjective optimization in combinatorial problems
    Optimization Methods and Software (IF2016 = 1.023), 31(2), pp. 392-404, Taylor & Francis, 2016.
    [BibTex]   [PDF]

    Supplementary material

    The Version of Record of this manuscript has been published and is available in Optimization Methods and Software 23 Dec 2015 http://www.tandfonline.com/doi/abs/10.1080/10556788.2015.1121485

2015
  • K. Michalak
    Improving the NSGA-II Performance with an External Population
    Intelligent Data Engineering and Automated Learning - IDEAL 2015, Lecture Notes in Computer Science, volume 9375, pp. 273-280, Springer, 2015.
    [BibTex]   [PDF]   [Presentation]

    The original publication is available at www.springerlink.com
    Cited by: [24,73]

  • K. Michalak
    Local Search Based on a Local Utopia Point for the Multiobjective Travelling Salesman Problem
    Intelligent Data Engineering and Automated Learning - IDEAL 2015, Lecture Notes in Computer Science, volume 9375, pp. 281-289, Springer, 2015.
    [BibTex]   [PDF]   [Presentation]

    The original publication is available at www.springerlink.com

  • K. Michalak
    Optimization of Poincaré sections for discriminating between stochastic and deterministic behavior of dynamical systems
    Chaos, Solitons & Fractals (IF2015 = 1.611), 78, pp. 215-228, Elsevier, 2015.
    [BibTex]   [PDF]
    The original publication is available at Elsevier
    Cited by: [10]

  • K. Michalak
    Selecting Best Investment Opportunities from Stock Portfolios Optimized by a Multiobjective Evolutionary Algorithm
    GECCO'15 Proceedings of the 2015 Genetic and Evolutionary Computation Conference, pp. 1239-1246, ACM, 2015.
    Best Paper nomination at the Real World Applications track
    [BibTex]   [Presentation]
    Cited by: [25,26,29]

  • P. Filipiak, K. Michalak, P. Lipinski
    Infeasibility Driven Evolutionary Algorithm with the Anticipation Mechanism for the Reaching Goal in Dynamic Constrained Inverse Kinematics
    GECCO'15 Proceedings of the Companion Publication of the 2015 Genetic and Evolutionary Computation Conference, pp. 1389-1390, ACM, 2015.
    [BibTex]

  • K. Michalak
    Using an outward selective pressure for improving the search quality of the MOEA/D algorithm
    Computational Optimization and Applications (IF2015 = 1.444), 61(3), pp. 571-607, Springer, 2015.
    [BibTex]
    This paper is available as open access here
    Cited by: [15]

  • K. Michalak
    The Sim-EA Algorithm with Operator Autoadaptation for the Multiobjective Firefighter Problem
    15th European Conference, EvoCOP 2015, Copenhagen, Denmark, April 8-10, 2015, Proceedings, Lecture Notes in Computer Science, volume 9026, pp. 184-196, Springer, 2015.
    [BibTex]   [PDF]   [Presentation]

    The original publication is available at www.springerlink.com

2014
  • K. Michalak
    The effects of asymmetric neighborhood assignment in the MOEA/D algorithm
    Applied Soft Computing (IF2014 = 2.810), volume 25, pp. 97-106, Elsevier, 2014.
    [BibTex]   [PDF]

    The original publication is available at Elsevier
    Cited by: [104,114,127,129,139]

  • K. Michalak
    Analysis of Dynamic Properties of Stock Market Trading Experts Optimized with an Evolutionary Algorithm
    17th European Conference, EvoApplications 2014, Granada, Spain, April 23-25, 2014, Revised Selected Papers, Lecture Notes in Computer Science, volume 8602, pp. 264-275, Springer, 2014.
    [BibTex]   [PDF]   [Presentation]

    The original publication is available at www.springerlink.com
    Cited by: [106]

  • K. Michalak
    Sim-EA: An Evolutionary Algorithm Based on Problem Similarity
    Intelligent Data Engineering and Automated Learning - IDEAL 2014, Lecture Notes in Computer Science, volume 8669, pp. 191-198, Springer, 2014.
    [BibTex]   [PDF]   [Presentation]

    The original publication is available at www.springerlink.com

  • K. Michalak, P. Filipiak, P. Lipiński
    Multiobjective Dynamic Constrained Evolutionary Algorithm for Control of a Multi-Segment Articulated Manipulator
    Intelligent Data Engineering and Automated Learning - IDEAL 2014, Lecture Notes in Computer Science, volume 8669, pp. 199-206, Springer, 2014.
    [BibTex]   [PDF]   [Presentation]

    The original publication is available at www.springerlink.com

  • A. Łańcucki, J. Chorowski, K. Michalak, P. Filipiak, P. Lipiński
    Continuous Population-Based Incremental Learning with Mixture Probability Modeling for Dynamic Optimization Problems
    Intelligent Data Engineering and Automated Learning - IDEAL 2014, Lecture Notes in Computer Science, volume 8669, pp. 457-464, Springer, 2014.
    [BibTex]   [PDF]

    The original publication is available at www.springerlink.com

  • K. Michalak
    Auto-adaptation of Genetic Operators for Multi-objective Optimization in the Firefighter Problem
    Intelligent Data Engineering and Automated Learning - IDEAL 2014, Lecture Notes in Computer Science, volume 8669, pp. 484-491, Springer, 2014.
    [BibTex]   [PDF]   [Presentation]

    The original publication is available at www.springerlink.com

2013
  • P. Hájek, K. Michalak
    Feature selection in corporate credit rating prediction
    Knowledge-Based Systems (IF2013 = 3.058), volume 51, pp. 72-84, Elsevier 2013.
    Cited by: [1,7,16,22,23,30,41,42,43,44,46,47,51,54,56,57,60,61,63,67,69,87,91,93,97,107,130,131,132,134,

  • K. Michalak, P. Filipiak, P. Lipiński
    Usage Patterns of Trading Rules in Stock Market Trading Strategies Optimized with Evolutionary Methods
    16th European Conference, EvoApplications 2013, Vienna, Austria, April 3-5, 2013, Lecture Notes in Computer Science, volume 7835, pp. 234-243, Springer, 2013.
    [BibTex]   [PDF]   [Presentation]

    The original publication is available at www.springerlink.com
    Cited by: [25,26]

2012
  • K. Michalak, P. Filipiak, P. Lipiński
    Evolutionary Approach to Multiobjective Optimization of Portfolios That Reflect the Behaviour of Investment Funds
    Artificial Intelligence: Methodology, Systems, and Applications, Lecture Notes in Computer Science, volume 7557, pp. 202-211, Springer, 2012.
    [BibTex]   [PDF]

    The original publication is available at www.springerlink.com

  • P. Filipiak, K. Michalak, P. Lipiński
    A Predictive Evolutionary Algorithm for Dynamic Constrained Inverse Kinematics Problems
    Hybrid Artificial Intelligence Systems, Lecture Notes in Computer Science, volume 7208, pp. 610-621. Springer, 2012.
    [BibTex]   [PDF]

    The original publication is available at www.springerlink.com
    Cited by: [21]

2011
  • K. Michalak, J. Korczak
    Evolutionary Graph Mining in Suspicious Transaction Detection
    Advanced Information Technologies for Management - AITM 2011, 206:120-129, 2011.
    [BibTex]

  • K. Michalak, H. Kwaśnicka, E. Watorek, M. Klinger
    Selection of Numerical and Nominal Features Based on Probabilistic Dependence Between Features
    Applied Artificial Intelligence (IF2011 = 0.333), 25(8), Taylor & Francis, pp. 746–767, 2011.
    Cited by: [85]

    The Version of Record of this manuscript has been published and is available in Applied Artificial Intelligence 20 Sep 2011 http://www.tandfonline.com/doi/abs/10.1080/08839514.2011.607014

    [BibTex]   [PDF]

  • K. Michalak, J. Korczak
    Graph Mining Approach to Suspicious Transaction Detection
    Proceedings of the Federated Conference on Computer Science and Information Systems, FedCSIS 2011, E-ISBN: 978-83-60810-35-4, Print ISBN: 978-1-4577-0041-5, pp. 69-75, 2011.
    Cited by: [14,18,27,48,84,94,95,102,110,117,119]
    [BibTex]

  • P. Filipiak, K. Michalak, P. Lipiński
    Infeasibility Driven Evolutionary Algorithm with ARIMA-Based Prediction Mechanism
    Intelligent Data Engineering and Automated Learning - IDEAL 2011, Lecture Notes in Computer Science, volume 6936, pp. 345-352. Springer-Verlag, Berlin Heidelberg, 2011.
    Cited by: [31,32,33,116,144,145]
    [BibTex]

  • K. Michalak, B. Dzieńkowski, E. Hudyma, M. Stanek
    Analysis of Inter-rater Agreement among Human Observers Who Judge Image Similarity
    Computer Recognition Systems 4, Advances in Intelligent and Soft Computing, ISBN: 978-3-642-20319-0, volume 95, pp. 249-258, 2011.
    Cited by: [74]
    [BibTex]

2010
  • K. Michalak, H. Kwaśnicka
    Correlation Based Feature Selection Method
    International Journal of Bio-Inspired Computation, 2(5), pp. 319-332, 2010.
    Cited by: [13,36,39,52,72,88,108,126,135]

  • K. Michalak
    Mieszana metoda wyboru cech do zadania klasyfikacji
    Praca doktorska wykonana na Politechnice Wrocławskiej pod kierunkiem prof. dr hab. inż. Haliny Kwaśnickiej.
2007
  • K. Michalak, H. Kwaśnicka
    Influence of data dimensionality on the quality of forecasts given by a multilayer perceptron
    Theoretical Computer Science (IF2007 = 0.735), 371(1-2), pp. 62-71, 2007.
    Cited by: [66,121]

  • K. Michalak, R. Raciborski
    Dynamic correlation approach to early stopping in neural forecasting of macroeconomic indices
    Journal of Applied Computer Science, 15(2), pp. 27-40, 2007.
2006
  • K. Michalak, H. Kwaśnicka
    Correlation-based feature selection strategy in classification problems
    International Journal of Applied Mathematics and Computer Science, 16(4), pp. 503-511, 2006.
    Cited by: [4,19,20,28,37,49,62,65,64,71,80,81,82,83,86,92,96,98,103,105,109,118,120,122,137,138,141,142,147,149,

  • K. Michalak, H. Kwaśnicka
    Correlation-based feature selection strategy in neural classification
    Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications (ISDA 2006), October 16-18, 2006, Jinan, China, pp. 741-746. IEEE Computer Society, 2006.
    Cited by: [3,5,6,9,11,12,17,35,38,53,55,58,59,68,75,76,77,78,90,99,100,101,111,112,113,123,128,133,146]
2005
  • K. Michalak, P. Lipinski
    Prediction of high increases in stock prices using neural networks
    Neural Network World, 15(4), pp. 359-366, 2005.
    Cited by: [2,8,40,45,79,89,124,125]

  • K. Michalak, H. Kwaśnicka
    Correlation dimension and the quality of forecasts given by a neural network
    S. B. Cooper, B. Löwe, and L. Torenvliet, ed., New Computational Paradigms, First Conference on Computability in Europe, CiE 2005, Amsterdam, The Netherlands, June 8-12, 2005, Proceedings, volume 3526 of Lecture Notes in Computer Science, pp. 332-341. Springer, 2005.

  • K. Michalak, R. Raciborski
    Dynamic correlation approach to early stopping in artificial neural network training. macroeconomic forecasting example
    Proceedings of the Fifth International Conference on Intelligent Systems Design and Applications (ISDA 2005), 8-10 September 2005, Wroclaw, Poland, pp. 100-105. IEEE Computer Society, 2005.
    Cited by: [115]
2004
  • K. Michalak, P. Lipinski
    Prediction of high increases in stock prices using neural networks
    J. Antoch, ed., 16th Symposium on Computational Statistics CompStat 2004, pp. 1489-1496. Springer, 2004.

Articles by other Authors citing my papers

[1]
M. Z. Abedin, C. Guotai, and M. Bin. Credit default prediction of chinese small business: A neural network methodology. European Journal of Economics, Finance and Administrative Sciences, 77:32-50, 2015.
[2]
Z. Afroz, S. R. Das, D. Mishra, and S. Patnaik. Mutual Fund Performance Analysis Using Nature Inspired Optimization Techniques: A Critical Review, pages 734-745. Springer International Publishing, Cham, 2018.
[3]
S. Ahmed, M. Khan, and M. Shahjahan. A filter based feature selection approach using Lempel Ziv complexity. In D. Liu, H. Zhang, M. Polycarpou, C. Alippi, and H. He, editors, Advances in Neural Networks - ISNN 2011, volume 6676 of Lecture Notes in Computer Science, pages 260-269. Springer Berlin / Heidelberg, 2011.
[4]
F. Alrusayni and A. Aloraini. Poster: An empirical study to compare sequential forward selection vs. sequential backward elimination in ensemble feature selection setting. In 2014 Symposium on Data Mining and Applications (SDMA2014), 2014.
[5]
M. Alweshah, O. A. Alzubi, J. A. Alzubi, and S. Alaqeel. Solving attribute reduction problem using wrapper genetic programming. IJCSNS International Journal of Computer Science and Network Security, 16(5), 2016.
[6]
M. Alweshah, O. A. Alzubi, J. A. Alzubi, and S. Alaqeel. Solving time series classification problems using combined of support vector machine and neural network. International Journal of Data Analysis Techniques and Strategies, 16(5), 2016.
[7]
T. Arundina, M. A. Omar, and M. Kartiwi. The predictive accuracy of Sukuk ratings; multinomial logistic and neural network inferences. Pacific-Basin Finance Journal, in press, 2015.
[8]
S. A. J. Babulo, B. Janaki, and C. Jeeva. Stock market indices prediction with various neural network models. International Journal of Computer Science and Mobile Applications, 2(3):42-46, 2014.
[9]
A. Bansal. Empirical analysis of search based algorithms to identify change prone classes of open source software. Computer Languages, Systems & Structures, 47:211-231, 2017.
[10]
Ö. Baydaroglu, K. Koçak, and K. Duran. River flow prediction using hybrid models of support vector regression with the wavelet transform, singular spectrum analysis and chaotic approach. Meteorology and Atmospheric Physics, 2017.
[11]
A. Behjat, A. Mustapha, H. Nezamabadi-pour, M. Sulaiman, and N. Mustapha. A PSO-based feature subset selection for application of spam / non-spam detection. In S. Noah, A. Abdullah, H. Arshad, A. Abu Bakar, Z. A. Othman, S. Sahran, N. Omar, and Z. Othman, editors, Soft Computing Applications and Intelligent Systems, volume 378 of Communications in Computer and Information Science, pages 183-193. Springer Berlin Heidelberg, 2013.
[12]
A. R. Behjat, A. Mustapha, H. Nezamabadi-pour, N. Sulaiman, and N. Mustapha. Feature subset selection using binary gravitational search algorithm for intrusion detection system. In A. Selamat, N. T. Nguyen, and H. Haron, editors, Intelligent Information and Database Systems, volume 7803 of Lecture Notes in Computer Science, pages 377-386. Springer Berlin Heidelberg, 2013.
[13]
H. Beiping and Z. Wen. Fast human detection using motion detection and histogram of oriented gradients. Journal of Computers, 6(8):1597-1604, 2011.
[14]
L. S. Bershtein and A. A. Tselykh. A clique-based method for mining fuzzy graph patterns in anti-money laundering systems. In Proceedings of the 6th International Conference on Security of Information and Networks, pages 384-387, New York, NY, USA, 2013. ACM.
[15]
X. Bi and C. Wang. An improved NSGA-III algorithm based on objective space decomposition for many-objective optimization. Soft Computing, pages 1-28, 2016.
[16]
V. Bures. A method for simplification of complex group causal loop diagrams based on endogenisation, encapsulation and order-oriented reduction. Systems, 5(3), 2017.
[17]
S. Cazzaniga, F. Sassi, S. R. Mercuri, and L. Naldi. Prediction of clinical response to excimer laser treatment in vitiligo by using neural network models. Dermatology, 219(2):133-137, 2009.
[18]
A. Chadha and P. Kaur. Handling Smurfing Through Big Data, pages 459-470. Springer Singapore, Singapore, 2018.
[19]
C.-P. Chang, C.-P. Chu, and Y.-F. Yeh. Integrating in-process software defect prediction with association mining to discover defect pattern. Inf. Softw. Technol., 51(2):375-384, 2009.
[20]
Y. F. Chang, J. C. Lee, O. M. Rijal, and S. A. R. S. A. Bakar. Efficient online handwritten chinese character recognition system using a two-dimensional functional relationship model. Applied Mathematics and Computer Science, 20(4):727-738, 2010.
[21]
V. B. Chao Li, Souleymane Balla-Arabe and F. Yang. A predictive function optimization algorithm for multi-spectral skin lesion assessment. In Proceedings of 23rd European Signal Processing Conference (EUSIPCO 2015), pages 1631-1635. IEEE Computer Society, 2015.
[22]
A. Chaudhuri and S. K. Ghosh. Bankruptcy Prediction through Soft Computing based Deep Learning Technique. Springer International Publishing, Cham, 2017.
[23]
L. Cui, L. Bai, Y. Wang, X. Bai, Z. Zhang, and E. R. Hancock. P2P lending analysis using the most relevant graph-based features. In A. Robles-Kelly, M. Loog, B. Biggio, F. Escolano, and R. Wilson, editors, Structural, Syntactic, and Statistical Pattern Recognition: Joint IAPR International Workshop, S+SSPR 2016, Mérida, Mexico, November 29 - December 2, 2016, Proceedings, pages 3-14. Springer International Publishing, Cham, 2016.
[24]
V. Das, P. Karuppanan, V. Karthikeyan, S. Rajasekar, and A. K. Singh. Energy grid management, optimization and economic analysis of microgrid. In F. R. Islam, K. A. Mamun, and M. T. O. Amanullah, editors, Smart Energy Grid Design for Island Countries: Challenges and Opportunities, pages 289-325. Springer International Publishing, Cham, 2017.
[25]
I. Deplano, G. Squillero, and A. Tonda. Anatomy of a portfolio optimizer under a limited budget constraint. Evolutionary Intelligence, pages 1-12, 2016.
[26]
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