Optimisation of an exemplar oculomotor model using multi-objective genetic algorithms executed on a GPU-CPU combination
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[1] Olympia Roeva,et al. Influence of the population size on the genetic algorithm performance in case of cultivation process modelling , 2013, 2013 Federated Conference on Computer Science and Information Systems.
[2] Kalyanmoy Deb,et al. Multi-objective Optimisation Using Evolutionary Algorithms: An Introduction , 2011, Multi-objective Evolutionary Optimisation for Product Design and Manufacturing.
[3] J. A. Kuznecov. Elements of applied bifurcation theory , 1998 .
[4] Richard V Abadi,et al. Waveform characteristics in congenital nystagmus , 1987, Documenta Ophthalmologica.
[5] Celso Grebogi,et al. Extracting unstable periodic orbits from chaotic time series data , 1997 .
[6] Laurence R. Young,et al. Variable Feedback Experiments Testing a Sampled Data Model for Eye Tracking Movements , 1963 .
[7] I. Gottlob,et al. Living with nystagmus: a qualitative study , 2010, British Journal of Ophthalmology.
[8] Richard V Abadi,et al. Motor and sensory characteristics of infantile nystagmus , 2002, The British journal of ophthalmology.
[9] J. Thompson,et al. The prevalence of nystagmus: the Leicestershire nystagmus survey. , 2009, Investigative ophthalmology & visual science.
[10] Ron Shonkwiler,et al. Parallel Genetic Algorithms , 1993, ICGA.
[11] H. Collewijn,et al. Binocular co‐ordination of human horizontal saccadic eye movements. , 1988, The Journal of physiology.
[12] Lothar Thiele,et al. Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach , 1999, IEEE Trans. Evol. Comput..
[13] F. Takens. Detecting strange attractors in turbulence , 1981 .
[14] Conor J. Houghton,et al. Parameter estimation of neuron models using in-vitro and in-vivo electrophysiological data , 2015, Front. Neuroinform..
[15] Jacob Roll,et al. Systems biology: model based evaluation and comparison of potential explanations for given biological data , 2009, The FEBS journal.
[16] Lothar Thiele,et al. Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study , 1998, PPSN.
[17] Julio R. Banga,et al. Multicriteria global optimization for biocircuit design , 2014, BMC Systems Biology.
[18] Enrique Alba,et al. A study of master-slave approaches to parallelize NSGA-II , 2008, 2008 IEEE International Symposium on Parallel and Distributed Processing.
[19] Dong-Kyun Kim,et al. Search of optimal locations for species- or group-specific primer design in DNA sequences: Non-dominated Sorting Genetic Algorithm II (NSGA-II) , 2015, Ecol. Informatics.
[20] anonymous,et al. Comprehensive review , 2019 .
[21] John E. Stone,et al. OpenCL: A Parallel Programming Standard for Heterogeneous Computing Systems , 2010, Computing in Science & Engineering.
[22] D. Robinson. Eye Movement Control in Primates , 1968 .
[23] J. J. Eggermont,et al. Reconstruction of neural control signals for saccades based on an inverse method , 1985, Vision Research.
[24] R. Roy,et al. Multi-objective optimisation of the protein-ligand docking problem in drug discovery , 2006, GECCO.
[25] Daniel Guitton,et al. The use of system identification techniques in the analysis of oculomotor burst neuron spike train dynamics , 1996, Journal of Computational Neuroscience.
[26] Yohei Murakami,et al. Bayesian Parameter Inference and Model Selection by Population Annealing in Systems Biology , 2014, PloS one.
[27] B. Nevitt,et al. Coping With Chaos , 1991, Proceedings of the 1991 International Symposium on Technology and Society - ISTAS `91.
[28] M. Caramia,et al. Multi-objective Management in Freight Logistics: Increasing Capacity, Service Level and Safety with Optimization Algorithms , 2008 .
[29] L. Averbuch‐Heller. Acquired nystagmus , 1999, Current treatment options in neurology.
[30] R. Leigh,et al. The neurology of eye movements , 1984 .
[31] P. Pospichal. GPU-based Acceleration of the Genetic Algorithm , 2009 .
[32] David S. Broomhead,et al. Modelling of congenital nystagmus waveforms produced by saccadic system abnormalities , 2000, Biological Cybernetics.
[33] Mustafa Khammash,et al. Parameter Estimation and Model Selection in Computational Biology , 2010, PLoS Comput. Biol..
[34] J R Banga,et al. Multi-objective mixed integer strategy for the optimisation of biological networks. , 2010, IET systems biology.
[35] Joshua D. Knowles,et al. Multiobjective Optimization in Bioinformatics and Computational Biology , 2007, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[36] L. Dell’Osso,et al. Congenital nystagmus: hypotheses for its genesis and complex waveforms within a behavioral ocular motor system model. , 2004, Journal of vision.
[37] Marjan Mernik,et al. Parameter tuning with Chess Rating System (CRS-Tuning) for meta-heuristic algorithms , 2016, Inf. Sci..
[38] Guillermo A. Cecchi,et al. When the Optimal Is Not the Best: Parameter Estimation in Complex Biological Models , 2010, PloS one.
[39] Julio Ortega Lopera,et al. Comparison of parallel multi-objective approaches to protein structure prediction , 2011, The Journal of Supercomputing.
[40] Richard A. Watson,et al. Reducing Local Optima in Single-Objective Problems by Multi-objectivization , 2001, EMO.
[41] Stephen P. Boyd,et al. Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.
[42] J. V. Van Gisbergen,et al. Skewness of saccadic velocity profiles: a unifying parameter for normal and slow saccades. , 1987, Vision research.
[43] Antonio Pescapè,et al. Genetic Algorithm Modeling with GPU Parallel Computing Technology , 2012, WIRN.
[44] Tea Tusar,et al. Visualization of Pareto Front Approximations in Evolutionary Multiobjective Optimization: A Critical Review and the Prosection Method , 2015, IEEE Transactions on Evolutionary Computation.
[45] S J Schiff,et al. Periodic orbits: a new language for neuronal dynamics. , 1998, Biophysical journal.
[46] Shu Jing Zhou,et al. Review of Genetic Algorithm , 2011 .
[47] Kevin O’Regan,et al. Saccade size control in reading: Evidence for the linguistic control hypothesis , 1979, Perception & psychophysics.
[48] Irene Gottlob,et al. Congenital nystagmus: Randomized, controlled, double‐masked trial of memantine/gabapentin , 2007, Annals of neurology.
[49] L. Stark,et al. The main sequence, a tool for studying human eye movements , 1975 .
[50] Aaftab Munshi,et al. The OpenCL specification , 2009, 2009 IEEE Hot Chips 21 Symposium (HCS).
[51] I. Gottlob,et al. Nystagmus in childhood. , 2014, Pediatrics and neonatology.
[52] Jesper Tegnér,et al. Optimization in Biology Parameter Estimation and the Associated Optimization Problem , 2016 .
[53] C. Scudder,et al. The microscopic anatomy and physiology of the mammalian saccadic system , 1996, Progress in Neurobiology.
[54] Karline Soetaert,et al. Solving Ordinary Differential Equations in R , 2012 .
[55] S Forrest,et al. Genetic algorithms , 1996, CSUR.
[56] Melanie Mitchell,et al. An introduction to genetic algorithms , 1996 .
[57] Grebogi,et al. Detecting unstable periodic orbits in chaotic experimental data. , 1996, Physical review letters.
[58] Hong Sun,et al. Smolign: A Spatial Motifs-Based Protein Multiple Structural Alignment Method , 2012, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[59] Kaisa Miettinen,et al. Some Methods for Nonlinear Multi-objective Optimization , 2001, EMO.
[60] Dominik Straumann,et al. A re-examination of the time constant of the oculomotor neural integrator in human , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
[61] Luonan Chen,et al. Multi-equilibrium property of metabolic networks: SSI module , 2011, Proceedings of the 30th Chinese Control Conference.
[62] Jaap A. Kaandorp,et al. Inferring Drosophila gap gene regulatory network: a parameter sensitivity and perturbation analysis , 2009, BMC Systems Biology.
[63] Mikkel T. Jensen,et al. Helper-objectives: Using multi-objective evolutionary algorithms for single-objective optimisation , 2004, J. Math. Model. Algorithms.
[64] Maria Romano,et al. Characterisation of baseline oscillation in congenital nystagmus eye movement recordings , 2009, Biomed. Signal Process. Control..
[65] Kalyanmoy Deb,et al. A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..
[66] David S Broomhead,et al. A new framework for investigating both normal and abnormal eye movements. , 2002, Progress in brain research.
[67] S. Gielen,et al. A quantitative analysis of generation of saccadic eye movements by burst neurons. , 1981, Journal of neurophysiology.
[68] Kaisa Miettinen,et al. Nonlinear multiobjective optimization , 1998, International series in operations research and management science.
[69] Wen-mei W. Hwu,et al. Optimization principles and application performance evaluation of a multithreaded GPU using CUDA , 2008, PPoPP.
[70] Qingfu Zhang,et al. Multiobjective evolutionary algorithms: A survey of the state of the art , 2011, Swarm Evol. Comput..
[71] Nick Fogt,et al. The Neurology of Eye Movements, 3rd ed. , 2000 .
[72] Mohamad Zoinol Abidin Abdul Aziz,et al. A review of Genetic Algorithms and Parallel Genetic Algorithms on Graphics Processing Unit (GPU) , 2013, 2013 IEEE International Conference on Control System, Computing and Engineering.
[73] A. Fuchs,et al. The brainstem burst generator for saccadic eye movements , 2002, Experimental Brain Research.
[74] R. B. Daroff,et al. Congenital nystagmus waveforms and foveation strategy , 1975, Documenta Ophthalmologica.
[75] Richard A. Clement,et al. Fixed point analysis of nystagmus , 2007, Journal of Neuroscience Methods.
[76] D. Robinson,et al. Loss of the neural integrator of the oculomotor system from brain stem lesions in monkey. , 1987, Journal of neurophysiology.
[77] A. V. van Opstal,et al. Dynamic ensemble coding of saccades in the monkey superior colliculus. , 2006, Journal of neurophysiology.
[78] Akito Taneda,et al. MODENA: a multi-objective RNA inverse folding , 2010, Advances and applications in bioinformatics and chemistry : AABC.
[79] P. Wurtz,et al. Size Matters: Saccades during Scene Perception , 2007, Perception.
[80] Jonathan M. Garibaldi,et al. Parameter Estimation Using Metaheuristics in Systems Biology: A Comprehensive Review , 2012, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[81] L. Optican,et al. A hypothetical explanation of congenital nystagmus , 1984, Biological Cybernetics.
[82] J. Harbison,et al. The Neurology of Eye Movements, 3rd ed , 2000 .
[83] K Aoki,et al. 5. Visualization of separation points and wake at smooth ball and dimpled balls , 2001 .
[84] Jerald B. Johnson,et al. Model selection in ecology and evolution. , 2004, Trends in ecology & evolution.
[85] Roberto Antonio Vázquez,et al. Tuning the parameters of an integrate and fire neuron via a genetic algorithm for solving pattern recognition problems , 2015, Neurocomputing.
[86] Masanori Arita,et al. Metabolomic correlation-network modules in Arabidopsis based on a graph-clustering approach , 2011, BMC Systems Biology.
[87] R A Clement,et al. Eye movement instabilities and nystagmus can be predicted by a nonlinear dynamics model of the saccadic system , 2005, Journal of mathematical biology.
[88] Ozgur E. Akman,et al. Dizzy-Beats: a Bayesian evidence analysis tool for systems biology , 2015, Bioinform..
[89] Maria Theodorou,et al. Classification of infantile nystagmus waveforms , 2016, Vision Research.
[90] Jasbir S. Arora,et al. Survey of multi-objective optimization methods for engineering , 2004 .
[91] Henry Markram,et al. A Novel Multiple Objective Optimization Framework for Constraining Conductance-Based Neuron Models by Experimental Data , 2007, Front. Neurosci..
[92] David S. Broomhead,et al. Characterisation of congenital nystagmus waveforms in terms of periodic orbits , 2002, Vision Research.
[93] Holger Fröhlich,et al. Modeling ERBB receptor-regulated G1/S transition to find novel targets for de novo trastuzumab resistance , 2009, BMC Systems Biology.
[94] Maksat Ashyraliyev,et al. Systems biology: parameter estimation for biochemical models , 2009, The FEBS journal.
[95] Francesco Falciani,et al. A computational framework for gene regulatory network inference that combines multiple methods and datasets , 2011, BMC Systems Biology.
[96] Jonathan E. Fieldsend,et al. Visualizing Mutually Nondominating Solution Sets in Many-Objective Optimization , 2013, IEEE Transactions on Evolutionary Computation.