A clonal selection algorithm for the electro encephalography signals reconstruction

This paper describes an adaptation of the Clonal Selection Algorithm for the single objective Big Optimization problem “Big-Opt”. Indeed, the electroencephalography “EEG” signals are very sensitive to noising effects of artifacts caused by undesirable internal and external electric sources. the main purpose of the Big-OPT problem is to rebuild the recorded signals in the goal of removing the artifacts as maximum as possible. To this end, an optimization problem is defined. To solve it, a modified search strategy is studied and adapted for the Clonal Selection Algorithm in order to enhance its convergence abilities on large scale optimization. To test the performance of the proposed method, experiments have been conducted over the Big-OPT EEG datasets. A comparison with recent state of the art approaches is also included. The study exhibits the competitive performance of the proposed Clonal Selection Algorithm.

[1]  Jing Liu,et al.  A multi-agent genetic algorithm for big optimization problems , 2015, 2015 IEEE Congress on Evolutionary Computation (CEC).

[2]  Vincenzo Cutello,et al.  Real coded clonal selection algorithm for unconstrained global optimization using a hybrid inversely proportional hypermutation operator , 2006, SAC.

[3]  E. Walls-Esquivel,et al.  Electroencephalography (EEG) recording techniques and artefact detection in early premature babies , 2007, Neurophysiologie Clinique/Clinical Neurophysiology.

[4]  Hitoshi Iba,et al.  Evolutionary Computation in Gene Regulatory Network Research , 2016 .

[5]  I. Halcu,et al.  A big data implementation based on Grid computing , 2013, 2013 11th RoEduNet International Conference.

[6]  Abdullah Al Mamun,et al.  Decompositional independent component analysis using multi-objective optimization , 2016, Soft Comput..

[7]  Ke Ding,et al.  Introduction to Fireworks Algorithm , 2013, Int. J. Swarm Intell. Res..

[8]  Seref Sagiroglu,et al.  Big data: A review , 2013, 2013 International Conference on Collaboration Technologies and Systems (CTS).

[9]  Elham Parvinnia,et al.  Classification of EEG Signals using adaptive weighted distance nearest neighbor algorithm , 2014, J. King Saud Univ. Comput. Inf. Sci..

[10]  Abdullah Al Mamun,et al.  Evolutionary big optimization (BigOpt) of signals , 2015, 2015 IEEE Congress on Evolutionary Computation (CEC).

[11]  Guan Le,et al.  Survey on NoSQL database , 2011, 2011 6th International Conference on Pervasive Computing and Applications.

[12]  Ana de Almeida,et al.  New genetic algorithm approach for the min-degree constrained minimum spanning tree , 2017, Eur. J. Oper. Res..

[13]  F. Burnet.  A modification of jerne's theory of antibody production using the concept of clonal selection , 1976, CA: a cancer journal for clinicians.

[14]  F. Azuaje.  Artificial Immune Systems: A New Computational Intelligence Approach , 2003 .

[15]  Thomas Stützle,et al.  Ant Colony Optimization: Overview and Recent Advances , 2018, Handbook of Metaheuristics.

[16]  Serhat Duman,et al.  Optimal power flow using gravitational search algorithm , 2012 .

[17]  E. Curran,et al.  Learning to control brain activity: A review of the production and control of EEG components for driving brain–computer interface (BCI) systems , 2003, Brain and Cognition.

[18]  Ruhul A. Sarker,et al.  Differential evolution framework for big data optimization , 2016, Memetic Comput..

[19]  A. Gandomi,et al.  A novel improved accelerated particle swarm optimization algorithm for global numerical optimization , 2014 .