Genetic Algorithms, a Nature-Inspired Tool: A Survey of Applications in Materials Science and Related Fields: Part II

REFERENCES

• Holland , J.H. Adaptation in Natural and Artificial Systems ; The University of Michigan Press : Ann Arbor , MI , USA , 1975 .
   Google Scholar
• Hamming , R.W. Error detecting and error correcting codes . Bell System Technical Journal 1950 , 29 , 147 – 160 .
   Google Scholar
• Beyer , W.A. ; Sellers , P.H. ; Waterman , M.S. ; Stanislaw , M. Ulam's contributions to theoretical theory . Letters in Mathematical Physics 1985 , 10 , 231 – 242 .
   Web of Science ®Google Scholar
• Ciesielski , K. ; Rassias , T.M. On Stan Ulam and his mathematics . The Australian Journal of Mathematical Analysis and Applications (AJMAA) 2009 , 6 , 1 – 9 .
   Google Scholar
• Menon , C. ; Lan , N. Potential biomimetic space systems enabled by micro technology. Proc. CANEUS–MNT for Aerospace Applications, August 27, 2006, Toulouse, France.
   Google Scholar
• Goldberg , D.E. Genetic Algorithms in Search, Optimization and Machine Learning ; Addison-Wesley : Reading , MA , 1989 .
   Google Scholar
• Forrest , S. Genetic algorithms: Principles of natural selection applied to computation . Science 1993 , 261 ( 5123 ), 872 – 878 .
   PubMed Web of Science ®Google Scholar
• Baeck , T. ; Fogel , D. ; Michalewicz , Z. Handbook of Evolutionary Computation ; Oxford University Press and Institute of Physics : Oxford and London , 1997 .
   Google Scholar
• Arabas , J. Wykłady z algorytmów ewolucyjnych (Lectures on Evolutionary Algorithms) ; (in Polish); , 2nd Ed. , Wydawnictwa Naukowo-Techniczne : Warsaw , 2004 .
   Google Scholar
• Sivanandam , S.N. ; Deepa , S.N. Introduction to Genetic Algorithms ; Springer-Verlag : Berlin–Heidelberg , 2008 .
   Google Scholar
• Affenzeller , M. ; Wagner , S. Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications ; Chapman & Hall/CRC : Boca Raton , FL , USA , 2009 .
   Google Scholar
• Alander , J.T. An Indexed Bibliography of Genetic Algorithms in Materials Science and Engineering, Department of Electrical Engineering and Automation University of Vaasa, Vaasa, Finland, Report Series No. 94-1-MSE (DRAFT 2008/06/11 12:26), available at http://www.mmnt.net/db/0/3/ftp.uwasa.fi/cs/report94-1/gaMSEbib.pdf
   Google Scholar
• Ohno , K. ; Esfarjani , K. ; Kawazoe , Y. Computational Materials Science: From Ab Initio to Monte Carlo Methods ; Springer Verlag : Berlin , Heidelberg, New York , 1999 .
   Google Scholar
• Chakraborti , N. Editorial: The genetic path of materials research . Materials and Manufacturing Processes 2003 , 18 , vii – viii .
   Web of Science ®Google Scholar
• Paszkowicz , W. Genetic algorithms, a nature-inspired tool: Survey of applications in materials science and related fields . Materials and Manufacturing Processes 2009 , 24 , 174 – 197 .
   Web of Science ®Google Scholar
• Chakraborti , N. Genetic algorithms in materials design and processing. International Materials Reviews 2004, 49, 246–260.
   Web of Science ®Google Scholar
• Sastry , K. Genetic Algorithms and Genetic Programming for Multiscale Modeling: Applications in Materials Science and Chemistry and Advances in Scalability, IlliGAL Report No. 2007019, September, 2007 (Illinois Genetic Algorithms Laboratory, University of Illinois at Urbana-Champaign, Urbana, Illinois), Ph.D. Dissertation.
   Google Scholar
• Koljonen , J. ; Nordling , T. ; Alander , J. A review of genetic algorithms in near infrared spectroscopy and chemometrics: Past and future . Journal of Near Infrared Spectroscopy 2008 , 16 , 189 – 197 .
   Web of Science ®Google Scholar
• Mitra , K. Genetic algorithms in polymeric material production, design, processing and other applications: A review . International Materials Reviews 2008 , 53 , 275 – 297 .
   Web of Science ®Google Scholar
• Woodley , S.M. ; Catlow , R. Crystal structure prediction from first principles . Nature Materials 2008 , 7 , 937 – 946 .
   PubMed Web of Science ®Google Scholar
• Coello Coello , C.A. ; Landa Becerra , R. Evolutionary multiobjective optimization in materials science and engineering . Materials and Manufacturing Processes 2009 , 24 , 119 – 129 .
   Web of Science ®Google Scholar
• Ye , Z. ; Li , Z. ; Min , M. Some improvements on adaptive genetic algorithms for reliability-related applications . Reliability Engineering and System Safety 2010 , 95 , 120 – 126 .
   Web of Science ®Google Scholar
• Yusup , N. ; Zain , A.M. ; Hashim , S.Z.M. Evolutionary techniques in optimizing machining parameters: Review and recent applications (2007–2011) . Expert Systems with Applications 2012 , 39 , 9909 – 9927 .
   Web of Science ®Google Scholar
• Niazi , A. ; Leardi , R. Genetic algorithms in chemometrics . Journal of Chemometrics 2012 , 26 , 345 – 351 .
   Web of Science ®Google Scholar
• Alander , J.T. An Indexed Bibliography of Genetic Algorithms and X-Rays. University of Vaasa, Vaasa, Finland, Report No. 94-1-X, Updated 2010/10/22 available at http://www.mmnt.net/db/0/3/ftp.uwasa.fi/cs/report94-1/gaXbib.pdf
   Google Scholar
• Odejobi , O.A. ; Umoru , L.E. Applications of soft computing techniques in materials engineering: A review . African Journal of Mathematics and Computer Science Research 2009 , 2 , 104 – 131 .
   Google Scholar
• Maier , W.F. ; Stöwe , K. ; Sieg , S. Combinatorial and high-throughput materials science . Angewandte Chemie International Edition 2007 , 46 , 6016 – 6067 .
   PubMed Web of Science ®Google Scholar
• Asadian , M. ; Seyedein , S.H. ; Aboutalebi , M.R. ; Maroosi , A. Optimization of the parameters affecting the shape and position of crystal–melt interface in YAG single crystal growth . Journal of Crystal Growth 2009 , 311 , 342 – 348 .
   Web of Science ®Google Scholar
• Lum , K.-Y. ; Jacquart , P.-M. ; Sefrioui , M. Constrained optimization of multilayered anti-reflection coatings using genetic algorithms. Proceedings of the 4th Asia-Pacific Conference on Simulated Evolution and Learning-coating (SEAL'02), vol.1 172–177 (Nanyang Technical University, Singapore 2002).
   Google Scholar
• Ko , Y.-D. ; Moon , P. ; Kim , C.E. ; Ham , M.-H. ; Myoung , J.-M. ; Yun , I. Modeling and optimization of the growth rate for ZnO thin films using neural networks and genetic algorithms . Expert Systems with Applications 2009 , 36 , 4061 – 4066 .
   Web of Science ®Google Scholar
• Kim , B. ; Lee , H. ; Kim , D. Modeling of thin film process data using a genetic algorithm-optimized initial weight of backpropagation neural network . Applied Artificial Intelligence: An International Journal 2009 , 23 , 168 – 178 .
   Web of Science ®Google Scholar
• Schubert , M.F. ; Mont , F.W. ; Chhajed , S. ; Poxson , D.J. ; Kim , J.K. ; Schubert , E.F. Design of multilayer antireflection coatings made from co-sputtered and low-refractive-index materials by genetic algorithm . Optics Express 2008 , 16 , 5290 – 5298 .
   PubMed Web of Science ®Google Scholar
• Poxson , D.J. ; Kuo , M.L. ; Mont , F.W. ; Kim , Y.S. ; Yan , X. ; Welser , R.E. ; Sood , A.K. ; Cho , J. ; Lin , S.Y. ; Schubert , E.F. High-performance antireflection coatings utilizing nanoporous layers . MRS Bulletin 2011 , 36 , 434 – 438 .
   Web of Science ®Google Scholar
• Rajak , P. ; Ghosh , S. ; Bhattacharya , B. ; Chakraborti , N. Pareto-optimal analysis of Zn-coated Fe in the presence of dislocations using genetic algorithms . Computational Materials Science 2012 , 62 , 266 – 271 .
   Web of Science ®Google Scholar
• Davies , T.E.B. ; Mehta , D.P. ; Rodríguez-López , J.L. ; Gilmer , G.H. ; Ciobanu , C.V. A variable-number genetic algorithm for growth of 1-dimensional nanostructures into their global minimum configuration under radial confinement . Materials and Manufacturing Processes 2009 , 24 , 265 – 273 .
   Web of Science ®Google Scholar
• Nagesh , D.S. ; Datta , G.L. Genetic algorithm for optimization of welding variables for height to width ratio and application of ANN for prediction of bead geometry for TIG welding process . Applied Soft Computing 2010 , 10 , 897 – 907 .
   Web of Science ®Google Scholar
• Chandrasekhar , N. ; Vasudevan , M. Intelligent modeling for optimization of A-TIG welding process . Materials and Manufacturing Processes 2010 , 25 , 1341 – 1350 .
   Web of Science ®Google Scholar
• Bandaru , S. ; Tutum , C.C. ; Deb , K. ; Hattel , J.H. Higher-level Innovization: A case study from friction stir welding process optimization. Proc. of the 2011 IEEE Congress on Evolutionary Computation (CEC), June 5–8, 2011, pp. 2782–2789.
   Google Scholar
• Tutum , C.C. ; Deb , K. ; Hattel , J.H. Hybrid search for faster production and safer process conditions in friction stir welding. Lecture Notes in Computer Science . Proceedings of Simulated Evolution and Learning , Indian Intitute of Technology Kanpur, India , 2011 , 6457 , 603 – 612 .
   Google Scholar
• Bhargava , S. ; Dulikravich , G.S. ; Murty , G.S. ; Agarwal , A. ; Colaço , M.J. Stress corrosion cracking resistant aluminum alloys: Optimizing concentrations of alloying elements and tempering . Materials and Manufacturing Processes 2011 , 26 , 363 – 374 .
   Web of Science ®Google Scholar
• Cheung , T. ; Cheung , N. ; Tobar , C.M.T. ; Caram , R. ; Garcia , A. Application of a genetic algorithm to optimize purification in the zone refining process . Materials and Manufacturing Processes 2011 , 26 , 493 – 500 .
   Web of Science ®Google Scholar
• Rivera-Diaz-del-Castillo , P.E.J. ; Xu , W. Heat treatment and composition optimization of nanoprecipitation hardened alloys . Materials and Manufacturing Processes 2011 , 26 , 375 – 381 .
   Web of Science ®Google Scholar
• Sun , Y. ; Zeng , W.D. ; Han , Y.F. ; Ma , X. ; Zhao , Y.Q. Optimization of chemical composition for TC11 titanium alloy based on artificial neural network and genetic algorithm . Computational Materials Science 2011 , 50 , 1064 – 1069 .
   Web of Science ®Google Scholar
• Kumar , A. ; Chakrabarti , D. ; Chakraborti , N. Data-driven Pareto optimization for microalloyed steels using genetic algorithms . Steel Research International 2012 , 83 , 169 – 174 .
   Web of Science ®Google Scholar
• Xu , W. ; Rivera-Díaz-del-Castillo , P.E.J. ; van der Zwaag , S. Designing nanoprecipitation strengthened UHS stainless steels combining genetic algorithms and thermodynamics . Computational Materials Science 2008 , 44 , 678 – 689 .
   Web of Science ®Google Scholar
• Tancret , F. Computational thermodynamics and genetic algorithms to design affordable γ'-strengthened nickel-iron based superalloys. Modelling and Simulation in Materials Science and Engineering 2012, 20, 045012.
   Web of Science ®Google Scholar
• Garcia , S. Experimental Design Optimization and Thermophysical Parameter Estimation of Composite Materials Using Genetic Algorithms. Ph.D. Dissertation, Université de Nantes, Nantes, France, 1999.
   Google Scholar
• Forlin , M. ; Poli , I. ; De March , D. ; Hanczyc , M. ; Packard , N. Serra , R. Evolving the experimental design for self-assembling amphiphilic systems . Chemometrics and Intelligent Laboratory Systems 2008 , 90 , 153 – 160 .
   Web of Science ®Google Scholar
• Yelk , J. ; Sukharev , M. ; Seideman , T. Optimal design of nanoplasmonic materials using genetic algorithms as a multiparameter optimization tool . The Journal of Chemical Physics 2008 , 129 , 064706 .
   Google Scholar
• Menon , V. ; Spruston , N. ; Kath , W.L. A state-mutating genetic algorithm to design ion-channel models . PNAS 2009 , 106 ( 39 ), 16829 – 16834 .
   PubMed Web of Science ®Google Scholar
• Dems , K. ; Wisniewski , J. Optimal design of fiber-reinforced composite disks . Journal of Theoretical and Applied Mechanics 2009 , 47 , 515 – 535 .
   Web of Science ®Google Scholar
• Giro , R. ; Cyrillo , M. ; Galvão , D.S. Using artificial intelligence methods to design new conducting polymers . Materials Research 2003 , 6 , 523 – 528 .
   Google Scholar
• Kaur , A. ; Wazir , M. ; Garg , A. ; Bakshi , A.K. Designing of type I polymeric superlattices using genetic and ant algorithms: A comparative study . Indian Journal of Chemistry 2009 , 48A , 1652 – 1656 .
   Google Scholar
• Arora , V. ; Bakhshi , A.K. Molecular designing of novel ternary copolymers of donor-acceptor polymers using genetic algorithm . Chemical Physics 2010 , 373 , 307 – 312 .
   Web of Science ®Google Scholar
• Sharma , A.K. ; Son , K.H. ; Han , B.Y. ; Sohn , K.-S. Simultaneous optimization of luminance and color chromaticity of phosphors using a nondominated sorting genetic algorithm . Advanced Functional Materials 2010 , 20 , 1750 – 1755 .
   Web of Science ®Google Scholar
• Zalewski , R. ; Pyrz , M. Modeling and parameter identification of granular plastomer conglomerate submitted to internal underpressure . Engineering Structures 2010 , 32 , 2424 – 2431 .
   Web of Science ®Google Scholar
• Talar , J. ; Szeliga , D. ; Pietrzyk , M. Application of genetic algorithms for identification of rheological and friction parameters in copper deformation processes . Archives of Metallurgy 2002 , 47 , 27 – 41 .
   Google Scholar
• Fang , S.F. ; Wang , M.P. ; Qi , W.H. ; Zheng , F. Hybrid genetic algorithms and support vector regression in forecasting atmospheric corrosion of metallic materials . Computational Materials Science 2008 , 44 , 647 – 655 .
   Web of Science ®Google Scholar
• Jackiewicz , J. Calibration and evaluation of a combined fracture model of microvoid growth that may compete with shear in the polycrystalline microstructure by means of evolutionary algorithms. Proc. GAMS-2007 (Warsaw 2007) . Computational Materials Science 2009 , 45 , 133 – 149 .
   Web of Science ®Google Scholar
• Franulović , M. ; Basan , R. ; Prebil , I. Genetic algorithm in material model parameters' identification for low-cycle fatigue . Computational Materials Science 2009 , 45 , 505 – 510 .
   Web of Science ®Google Scholar
• Chua , A.L.S. ; Benedek , N.A. ; Chen , L. ; Finnis , M.W. ; Sutton , AP. A genetic algorithm for predicting the structures of interfaces in multicomponent systems . Nature Materials 2010 , 9 , 418 – 422 .
   PubMed Web of Science ®Google Scholar
• Yuge , K. Phase stability of boron carbon nitride in a heterographene structure: A first-principles study . Physical Review B 2009 , 79 , 144109 .
   Google Scholar
• Bhattacharya , B. ; Kumar , G.R.D. ; Agarwal , A. ; Erkoc , S. ; Singh , A. ; Chakraborti , N. Analyzing Fe-Zn system using molecular dynamics, evolutionary neural nets and multi-objective genetic algorithms . Computational Materials Science 2009 , 46 , 821 – 827 .
   Web of Science ®Google Scholar
• Brezocnik , M. ; Buchmeister , B. ; Gusel , L. Evolutionary algorithm approaches to modeling of flow stress . Materials and Manufacturing Processes 2011 , 26 , 501 – 507 .
   Web of Science ®Google Scholar
• Sharma , R. ; Nandy , S. ; Chaudhury , P. ; Bhattacharyya , S.P. A density-genetic algorithm method for computing electronic structures of doped and undoped polythiophene oligomers: A modified Su–Schrieffer–Heeger hamiltonian-based study . Materials and Manufacturing Processes 2011 , 26 , 354 – 362 .
   Web of Science ®Google Scholar
• Zhang , Q. ; Mahfouf , M. ; Yates , J.R. ; Pinna , C. ; Panoutsos , G. ; Boumaiza , S. ; Greene , R.J. ; de Leon , L. Modeling and optimal design of machining-induced residual stresses in aluminium alloys using a fast hierarchical multiobjective optimization algorithm . Materials and Manufacturing Processes 2011 , 26 , 508 – 520 .
   Web of Science ®Google Scholar
• Park , H. ; Fellinger , M.R. ; Lenosky , T.J. ; Tipton , W.W. ; Trinkle , D.R. ; Rudin , S.P. ; Woodward , C. ; Wilkins , J.W. ; Hennig , R.G. Ab initio based empirical potential used to study the mechanical properties of molybdenum . Physical Review B 2012 , 85 , 214121 .
   Google Scholar
• Bazarov , I.V. ; Senderovich , I. ; Sinclair , C.K. Use of multiobjective evolutionary algorithms in high brightness electron source design. Proc. 21st IEEE Particle Accelerator Conference, Knoxville, TN, USA, May 16–20, 2005, pp. 2188–2190.
   Google Scholar
• Kim , B. ; Kim , D. ; Baik , S.W. ; Lee , S.B. ; Kim , D.H. Use of neural network and genetic algorithm to model scanning electron microscopy for enhanced image of material surfaces . Materials and Manufacturing Processes 2011 , 26 , 382 – 387 .
   Web of Science ®Google Scholar
• Farrusseng , D. ; Clerc , F. ; Mirodatos , C. ; Rakotomalala , R. Virtual screening of materials using neuro-genetic approach: Concepts and implementation. Proc. GAMS-2007 (Warsaw 2007) . Computational Materials Science 2009 , 45 , 52 – 59 .
   Web of Science ®Google Scholar
• Czochralski , J. Ein Neues Verfahren zur Messung der Kristallisationsgeschwindigkeit der Metalle . 1918 , 92 , 219 – 221 .
   Google Scholar
• Dhanaraj , G. ; Byrappa , K. ; Prasad , V. ; Dudley , M. (Eds.). Springer Handbook of Crystal Growth ; Springer Verlag : Berlin, Heidelberg, New York , 2010 .
   Google Scholar
• Giro , R. ; Cyrillo , M. ; Galvao , D.S. Designing conducting polymers using genetic algorithms . Chemical Physics Letters 2002 , 366 , 170 – 175 .
   Web of Science ®Google Scholar
• Bhattacharya , B. ; Kumar , G.R.D. ; Agarwal , A. ; Erkoç , S. ; Singh , A. ; Chakraborti , N. Analyzing Fe–Zn system using molecular dynamics, evolutionary neural nets and multi-objective genetic algorithms . Computational Materials Science 2009 , 46 , 821 – 827 .
   Web of Science ®Google Scholar
• Tarasiuk , J. ; Wierzbanowski , K. ; Lodini , A. Use of genetic algorithms for optimisation of materials properties . Archives of Metallurgy and Materials 2009 , 54 , 35 – 39 .
   Web of Science ®Google Scholar
• Hooper , J. ; Ismail , A. ; Giorgi , J.B. ; Woo , T.K. Genetic algorithm based approach to investigate doped metal oxide materials: Application to lanthanide-doped ceria . Physical Review B 2010 , 81 , 224104 .
   Google Scholar
• Mahmoudi , A.H. ; Pezeshki-Najafabadi , S.M. ; Badnava , H. Parameter determination of Chaboche kinematic hardening model using a multi objective genetic algorithm. Computational Materials Science 2011, 50, 1114–1122.
   Web of Science ®Google Scholar
• Gwizdalla , T.M. Magnetic properties of ternary prussian blue analogs studied with genetic approach . Computational Materials Science 2011 , 50 , 2644 – 2650 .
   Web of Science ®Google Scholar
• Rauchs , G. ; Dumitriu , D. Indentation testing parameter identification using an optimization procedure based on genetic algorithms. Proceedings of The Romanian Academy A 2009, 10, 165–172.
   Google Scholar
• Jóhannesson , G.H. ; Bligaard , T. ; Ruban , A.V. ; Skriver , H.L. ; Jacobsen , K.W. ; Nørskov , J.K. Combined electronic structure and evolutionary search approach to materials design . Physical Review Letters 2002 , 88 , 255506 .
   PubMed Web of Science ®Google Scholar
• Staszewska , G. ; Staszewski , P. ; Zebrowski , K. On an improved model of a complex optical potential for electron elastic scattering . Journal of Electron Spectroscopy and Related Phenomena 2008 , 168 , 40 – 43 .
   Web of Science ®Google Scholar
• Dudiy , S.V. ; Zunger , A. Searching for alloy configurations with target physical properties: Impurity design via a genetic algorithm inverse band structure approach . Physical Review Letters 2006 , 97 , 046401 .
   Google Scholar
• Piquini , P. ; Graf , P.A. ; Zunger , A. Band-gap design of quaternary (In,Ga)(As,Sb) semiconductors via the inverse-band-structure approach . Physical Review Letters 2008 , 100 , 186403 .
   PubMedGoogle Scholar
• Korkusinski , M. ; Zielinski , M. ; Hawrylak , P. Multiexciton complexes in InAs self-assembled quantum dots . Journal of Applied Physics 2009 , 105 , 122406 .
   Google Scholar
• Arora , V. ; Bakhshi , A.K. Investigation of electronic structures of model polypeptide chains using genetic algorithm . Indian Journal of Chemistry Section A-Inorganic Bio-Inorganic Physical Theoretical and Analytical Chemistry 2010 , 49 , 18 – 25 .
   Web of Science ®Google Scholar
• Gajer , M. Determining the working points of bipolar transistors with the use of the evolutionary strategy . Przeglad Elektrotechniczny (Poland) 2011 , 87 , 124 – 128 .
   Web of Science ®Google Scholar
• d'Avezac , M. ; Luo , J.W. ; Chanier , T. ; Zunger , A. Genetic-algorithm discovery of a direct-gap and optically allowed superstructure from indirect-Gap Si and Ge semiconductors . Physical Review Letters 2012 , 108 , 027401 .
   Google Scholar
• Sanchis , P. ; Villalba , P. ; Cuesta , F. ; Håkansson , A. ; Griol , A. ; Galán , J.V. ; Brimont , A. ; Martí , J. Highly efficient crossing structure for siliconon-insulator waveguides . Optics Letters 2009 , 34 , 2760 – 2762 .
   PubMed Web of Science ®Google Scholar
• Jiang , L. ; Li , H. ; Jia , W. ; Li , X. ; Shen , Z. Genetic optimization of photonic crystal waveguide termination for both on-axis and off-axis highly efficient directional emission . Optics Express 2009 , 17 , 10126 – 10135 .
   PubMed Web of Science ®Google Scholar
• Zhang , X.J. ; Qiao , G.J. Simulated annealing algorithm and genetic algorithm for optimization design of multilayer optical thin-film coatings . Key Engineering Materials: Materials Engineering for Advanced Technologies 2011 , 480–481 , 1362 – 1367 .
   Google Scholar
• Ghanaatshoar , M. ; Alisafaee , H. Genetic optimization of magneto-optic Kerr effect in lossy cavity-type magnetophotonic crystals . Journal of Magnetism and Magnetic Materials 2011 , 323 , 1823 – 1826 .
   Web of Science ®Google Scholar
• Miloua , R. ; Kebbab , Z. ; Chiker , F. ; Sahraoui , K. ; Khadraoui , M. ; Benramdane , N. Determination of layer thickness and optical constants of thin films by using a modified pattern search method . Optics Letters 2012 , 37 ( 4 ), 449 – 451 .
   PubMed Web of Science ®Google Scholar
• Lin , A. ; Phillips , J. Optimization of random diffraction gratings in thin-film solar cells using genetic algorithms . Solar Energy Materials and Solar Cells 2008 , 92 , 1689 – 1696 .
   Web of Science ®Google Scholar
• Yang , W. ; Yu , H. ; Tang , J. ; Su , Y. ; Wan , Q. ; Wang , Y. Omnidirectional light absorption in thin film silicon solar cell with dual anti-reflection coatings . Solar Energy 2011 , 85 , 2551 – 2559 .
   Web of Science ®Google Scholar
• Hachmann , J. ; Olivares-Amaya , R. ; Atahan-Evrenk , S. ; Amador-Bedolla , C. ; Sanchez-Carrera , R.S. ; Gold-Parker , A. ; Vogt , L. ; Brockway , A.M. ; Aspuru-Guzik , A. The Harvard Clean Energy Project: Large-scale computational screening and design of organic photovoltaics on the world community grid . Journal of Physical Chemistry Letters 2011 , 2 , 2241 – 2251 .
   Web of Science ®Google Scholar
• Gwizdalla , T.M. ; Moneta , M.E. Mössbauer distribution fitting by using global optimization approach . Nuclear Instruments and Methods in Physics Research B 2012 , 279 , 205 – 207 .
   Web of Science ®Google Scholar
• Cieslak , J. ; Dubiel , S.M. Simultaneous analysis of several Mossbauer spectra . Acta Physica Polonica A 2008 , 114 , 1691 – 1705 .
   Web of Science ®Google Scholar
• Liebmann , B. ; Friedl , A. ; Varmuza , K. Determination of glucose and ethanol in bioethanol production by near infrared spectroscopy and chemometrics . Analytica Chimica Acta 2009 , 642 , 171 – 178 .
   PubMed Web of Science ®Google Scholar
• Domanowska , A. ; Adamowicz , B. ; Bidzinski , P. ; Klimasek , A. ; Szewczenko , J. ; Gutt , T. ; Przewlocki , H. Analysis of chemical shifts in Auger electron spectra versus sputtering time from passivated surfaces . Optica Applicata 2011 , 41 , 441 – 447 .
   Web of Science ®Google Scholar
• Alisafaee , H. ; Ghanaatshoar , M. Optimization of all-garnet magneto-optical magnetic field sensors with genetic algorithm . Applied Optics 2012 , 51 , 5144 – 5148 .
   PubMedGoogle Scholar
• Fernandez , M. ; Caballero , J. ; Fernandez , L. ; Sarai , A. Genetic algorithm optimization in drug design QSAR: Bayesian-regularized genetic neural networks (BRGNN) and genetic algorithm-optimized support vectors machines (GA-SVM) . Molecular Diversity 2011 , 15 , 269 – 289 .
   PubMed Web of Science ®Google Scholar
• Tafipolsky , M. ; Schmid , R. Systematic first principles parameterization of force fields for metal-organic frameworks using a genetic algorithm approach . Journal of Physical Chemistry B 2009 , 113 , 1341 – 1352 .
   PubMed Web of Science ®Google Scholar
• Amirjalayer , S. ; Tafipolsky , M. ; Schmid , R. Exploring network topologies of copper paddle wheel based metal–organic frameworks with a first-principles derived force field . Journal of Physical Chemistry C 2011 , 115 , 15133 – 15139 .
   Web of Science ®Google Scholar
• Amirjalayer , S. ; Snurr , R.Q. ; Schmid , R. Prediction of structure and properties of boron-based covalent organic frameworks by a first-principles derived force field . Journal of Physical Chemistry C 2012 , 116 , 4921 – 4929 .
   Web of Science ®Google Scholar
• Habibi-Yangjeh , A. ; Pourbasheer , E. ; Danandeh-Jenagharad , M. Application of principal component-genetic algorithm-artificial neural network for prediction acidity constant of various nitrogen-containing compounds in water . Monatshefte für Chemie 2009 , 140 , 15 – 27 .
   Web of Science ®Google Scholar
• Gajer , M. Implementation of evolutionary algorithms in the discipline of artificial chemistry . Przeglad Elektrotechniczny (Poland) 2011 , 87 , 198 – 202 .
   Web of Science ®Google Scholar
• Courcot , B. ; Bridgeman , A.J. Optimization of a molecular mechanics force field for polyoxometalates based on a genetic algorithm. Journal of Computational Chemistry 2011, 32, 240–247.
   PubMed Web of Science ®Google Scholar
• Rein , G. ; Lautenberger , C. ; Fernandez-Pello , A.C. Using genetic algorithms to derive the parameters of solid-phase combustion from experiments. Proc. 20th International Colloquium on the Dynamics of Explosions and Reactive Systems, Montreal, Canada, August 2005, pp. 1–4.
   Google Scholar
• Li , S. ; Li , F. Alopex-based evolutionary algorithm and its application to reaction kinetic parameter estimation . Computers and Industrial Engineering 2011 , 60 , 341 – 348 .
   Web of Science ®Google Scholar
• Mondal , D.N. ; Sarangi , K. ; Pettersson , F. ; Sen , P.K. ; Saxén , H. ; Chakraborti , N. Cu-Zn separation by supported liquid membrane analyzed through multi-objective genetic algorithms . Hydrometallurgy 2011 , 107 , 112 – 123 .
   Web of Science ®Google Scholar
• Chong , Y. ; Yan , A. ; Yang , X. ; Cai , Y. ; Chen , J. An optimum fermentation model established by genetic algorithm for biotransformation from crude polydatin to resveratrol . Applied Biochemistry and Biotechnology 2012 , 166 , 446 – 457 .
   PubMed Web of Science ®Google Scholar
• Brunetti , A. ; Bailo , D. ; Rossi Albertini , V. Selective energy dispersive diffraction peak fitting by using genetic algorithm . Journal of X-Ray Science and Technology 2010 , 18 , 339 – 352 .
   PubMed Web of Science ®Google Scholar
• Zurek , S.G. Genetic algorithm with peaks adaptive objective function used to fit the EPR powder spectrum . Applied Soft Computing 2011 , 11 , 1000 – 1007 .
   Web of Science ®Google Scholar
• Zhang , Y. ; Xu , J. ; Yuan , Z. ; Xu , H. ; Yu , Q. Artificial neural network-genetic algorithm based optimization for the immobilization of cellulase on the smart polymer Eudragit L-100 . Bioresource Technology 2010 , 101 , 3153 – 3158 .
   PubMed Web of Science ®Google Scholar
• Zhang , Y. ; Xu , J.-L. ; Yuan , Z.-H. ; Qi , W. ; Liu , Y.-Y. ; He , M.-C. Artificial intelligence techniques to optimize the EDC/NHS-mediated immobilization of cellulase on eudragit L-100 . International Journal of Molecular Science 2012 , 13 , 7952 – 7962 .
   PubMed Web of Science ®Google Scholar
• Tomczak , E.T. ; Kaminski , W.L. Application of genetic algorithms to determine heavy metal ions sorption dynamics on clinoptilolite bed . Chemical and Process Engineering-Inzynieria Chemiczna i Procesowa (Poland) 2012 , 33 , 103 – 116 .
   Web of Science ®Google Scholar
• Day , G.M. ; Cooper , T.G. ; Cruz-Cabeza , A.J. ; Hejczyk , K.E. ; Ammon , H.L. ; Boerrigter , S.X.M. ; Tan , J.S. ; Della Valle , R.G. ; Venuti , E. ; Jose , J. ; Gadre , S.R. ; Desiraju , G.R. ; Thakur , T.S. ; van Eijck , B.P. ; Facelli , J.C. ; Bazterra , V.E. ; Ferraro , M.B. ; Hofmann , D.W.M. ; Neumann , M.A. ; Leusen , F.J.J. ; Kendrick , J. ; Price , S.L. ; Misquitta , A.J. ; Karamertzanis , P.G. ; Welch , G.W.A. ; Scheraga , H.A. ; Arnautova , Y.A. ; Schmidt , M.U. ; van de Streek , J. ; Wolf , A.K. ; Schweizer , B. Significant progress in predicting the crystal structures of small organic molecules: A report on the fourth blind test . Acta Crystallographica B 2009 , B65 , 107 – 125 .
   Web of Science ®Google Scholar
• Oganov , A.R. ; Tipton , W.W. ; Hennig , R.G. Modern Methods of Crystal Structure Prediction ; Wiley-VCH : Weinheim , Germany , 2011 .
   Google Scholar
• Oganov , A.R. ; Glass , C.W. Evolutionary crystal structure prediction as a tool in materials design . Journal of Physics: Condensed Matter 2008 , 20 , 064210 .
   Google Scholar
• Oganov , A.R. ; Lyakhov , A.O. ; Valle , M. How evolutionary crystal structure prediction works—and why . Accounts of Chemical Research 2011 , 44 , 227 – 237 .
   PubMed Web of Science ®Google Scholar
• Yao , Y. ; Tse , J.S. ; Tanaka , K. Metastable high-pressure single-bonded phases of nitrogen predicted via genetic algorithm . Physical Review B 2008 , 77 , 052103 .
   Google Scholar
• Pettersson , F. ; Suh , C. ; Saxén , H. ; Rajan , K. ; Chakraborti , N. Analyzing sparse data for nitride spinels using data mining, neural networks, and multiobjective genetic algorithms . Materials and Manufacturing Processes 2009 , 24 , 2 – 9 .
   Web of Science ®Google Scholar
• Kim , S. ; Orendt , A.M. ; Ferraro , M.B. ; Facelli , J.C. Crystal structure prediction of flexible molecules using parallel genetic algorithms with a standard force field . Journal of Computational Chemistry 2009 , 30 , 1973 – 1985 .
   PubMed Web of Science ®Google Scholar
• Filion , L. ; Dijkstra , M. Prediction of binary hard-sphere crystal structures . Physical Review E 2009 , 79 , 046714 .
   Web of Science ®Google Scholar
• Lin , Z.Z. ; Chen , X. ; Yin , C. ; Tang , H. ; Hu , Y.C. ; Ning , X.J. Theoretical prediction of the growth and surface structure of Pt and Ni nanoparticles . Europhysics Letters 2011 , 96 , 66005 .
   Google Scholar
• Mohn , C.E. ; Stølen , S. ; Kob , W. Predicting the structure of alloys using genetic algorithms . Materials and Manufacturing Processes 2011 , 26 , 348 – 353 .
   Web of Science ®Google Scholar
• Dyrka , W. , Nebel , J.C. A stochastic context free grammar based framework for analysis of protein sequences . BMC Bioinformatics 2009 , 10 , 323 .
   PubMedGoogle Scholar
• Sierka , M. Synergy between theory and experiment in structure resolution of low-dimensional oxides . Progress in Surface Science 2010 , 85 , 398 – 434 .
   Web of Science ®Google Scholar
• Oakley , M.T. ; Wales , D.J. ; Johnston , R.L. Energy landscape and global optimization for a frustrated model protein . Journal of Physical Chemistry B 2011 , 115 , 11525 – 11529 .
   PubMed Web of Science ®Google Scholar
• Ji , M. ; Umemoto , K. ; Wang , C.-Z. ; Ho , K.-M. ; Wentzcovitch , R.M. Ultrahigh-pressure phases of H2O ice predicted using an adaptive genetic algorithm . Physical Review B 2011 , 84 , 220105(R) .
   Google Scholar
• Boyer , S.A.E. ; Grolier , J.-P.E. ; Yoshida , H. ; Haudin , J.-M. ; Chenot , J.-L. Thermodynamics and thermokinetics to model phase transitions of polymers over extended temperature and pressure ranges under various hydrostatic fluids . In Thermodynamics–Interaction Studies–Solids, Liquids and Gases ; Moreno-Pirajan , J.C. Ed.; InTech : Rijeka, New York, Shanghai , 2011 .
   Google Scholar
• Harris , K.D.M. Fundamentals and applications of genetic algorithms for structure solution from powder X-ray diffraction data. Proc. GAMS-2007 (Warsaw 2007) . Computational Materials Science 2009 , 45 , 16 – 20 .
   Web of Science ®Google Scholar
• Gomez , A. ; Kycia , S. Structure determination from powder diffraction data using a new intensity extraction algorithm . Journal of Applied Crystallography 2011 , 44 , 708 – 713 .
   Web of Science ®Google Scholar
• Chakraborti , N. Genetic Algorithms and related techniques for optimizing Si–H clusters: A merit analysis for differential evolution . In Differential Evolution, Natural Computing Series ; Springer : Heidelberg , Germany , 2005 ; 313 – 326 .
   Google Scholar
• Marques , J.M.C. ; Pereira , F.B. ; Leita , T. On the use of different potential energy functions in rare-gas cluster optimization by genetic algorithms: Application to argon clusters . Journal of Physical Chemistry A 2008 , 112 , 6079 – 6089 .
   PubMed Web of Science ®Google Scholar
• Dugan , N. ; Erkoç , Ş. Genetic Algorithm application to the structural properties of Si–Ge mixed clusters . Materials and Manufacturing Processes 2009 , 24 , 250 – 254 .
   Web of Science ®Google Scholar
• Woodley , S.M. ; Hamad , S. ; Catlow , C.R.A. Exploration of multiple energy landscapes for zirconia nanoclusters . Physical Chemistry Chemical Physics 2010 , 12 , 8454 – 8465 .
   PubMed Web of Science ®Google Scholar
• Walsh , A. ; Woodley , S.M. Evolutionary structure prediction and electronic properties of indium oxide nanoclusters. Physical Chemistry Chemical Physics 2010, 12, 8446–8453.
   PubMed Web of Science ®Google Scholar
• Kamieniarz , G. ; Sobczak , P. Genetic algorithm approach to calculation of the ground-state configurations of 2D clusters of non-uniformly charged classical particles. Proc. GAMS-2007 (Warsaw 2007) . Computational Materials Science 2009 , 45 , 122 – 126 .
   Web of Science ®Google Scholar
• Sobczak , P. ; Kucharski , L. ; Kamieniarz , G. Genetic algorithm approach to calculation of geometric configurations of 2D clusters of uniformly charged classical particles . Computer Physics Communications 2011 , 182 , 1900 – 1903 .
   Web of Science ®Google Scholar
• Tran , D.T. ; Johnston , R.L. Study of 40-atom Pt–Au clusters using a combined empirical potential-density functional approach . Proceedings of the Royal Society A 2011 , 467 , 2004 – 2019 .
   Google Scholar
• Logsdail , A.J. ; Li , Z.Y. ; Johnston , R.L. Development and optimization of a novel genetic algorithm for identifying nanoclusters from scanning transmission electron microscopy images . Journal of Computational Chemistry 2012 , 33 , 391 – 400 .
   PubMed Web of Science ®Google Scholar
• Heiles , S. ; Logsdail , A.J. ; Schafer , R. ; Johnston , R.L. Dopant-induced 2D-3D transition in small Au-containing clusters: DFT-global optimisation of 8-atom Au-Ag nanoalloys . Nanoscale 2012 , 4 , 1109 – 1115 .
   PubMed Web of Science ®Google Scholar
• Solookinejad , G. ; Rozatian , A.S.H. ; Habibi , M.H. ZnO thinfilm characterization by X-ray reflectivity optimization using genetic algorithm and Fourier transformation . Applied Surface Science 2011 , 258 , 260 – 264 .
   Web of Science ®Google Scholar
• Kim , C.E. ; Moon , P. ; Yun , I. ; Kim , S. ; Myoung , J.-M. ; Jang , H.W. ; Bang , J. Process estimation and optimized recipes of ZnO:Ga thinfilm characteristics for transparent electrode applications . Expert Systems with Applications 2011 , 38 , 2823 – 2827 .
   Web of Science ®Google Scholar
• Cho , E.N. ; Moon , P. ; Kim , C.E. ; Yun , I. Modeling and optimization of ITO/AI/ITO multilayer films characteristics using neural network and genetic algorithm . Expert Systems with Applications 2012 , 39 , 8885 – 8889 .
   Web of Science ®Google Scholar
• Chepulskii , R.V. ; Barabash , S.V. ; Zunger , A. Ab initio theory of phase stability and structural selectivity in Fe-Pd alloys . Physical Review B 2012 , 85 , 144201 .
   Google Scholar
• Hafner , J. ; Wolverton , C. ; Ceder , G. Toward computational materials design: The impact of density functional theory on materials research . MRS Bulletin 2006 , 31 , 659 – 665 .
   Web of Science ®Google Scholar
• Paszkowicz , W. Contribution of a hard-spheres packing model to the determination of binary alloy phase diagrams . Journal of Physics F: Metal Physics 1988 , 18 , 1761 – 1785 .
   Google Scholar
• Zhou , C.-C. ; Yin , G.-F. ; Hu , X.-B. Multi-objective optimization of material selection for sustainable products: Artificial neural networks and genetic algorithm approach . Materials and Design 2009 , 30 , 1209 – 1215 .
   Web of Science ®Google Scholar
• Xu , P. ; Zheng , J. ; Chen , H. ; Liu , P. Optimal design of high pressure hydrogen storage vessel, using an adaptive genetic algorithm . International Journal of Hydrogen Energy 2010 , 35 , 2840 – 2846 .
   Web of Science ®Google Scholar
• Fu , Z. ; Mo , J. ; Chen , L. ; Chen , W. Using genetic algorithm-back propagation neural network prediction and finite-element model simulation to optimize the process of multiple-step incremental air-bending forming of sheet metal . Materials and Design 2010 , 31 , 267 – 277 .
   Web of Science ®Google Scholar
• Pu , T.L. ; Huang , K.M. ; Wang , B. ; Yang , Y. Application of micro-genetic algorithm to the design of matched high gain patch antenna with zero-refractive-index metamaterial lens . Journal of Electromagnetic Waves and Applications 2010 , 24 , 1207 – 1217 .
   Web of Science ®Google Scholar
• Muc , A. ; Kedziora , P. Optimal design of smart laminated composite structures . Materials and Manufacturing Processes 2010 , 25 , 272 – 280 .
   Web of Science ®Google Scholar
• Gramegna , N. ; della Corte , E. ; Poles , S. Manufacturing process simulation for product design chain optimization . Materials and Manufacturing Processes 2011 , 26 , 527 – 533 .
   Web of Science ®Google Scholar
• Dubrovski , P.D. ; Brezocnik , M. The modelling of porous properties regarding PES/CV-blended nonwoven wipes . Fibers and Polymers 2012 , 13 , 363 – 370 .
   Web of Science ®Google Scholar
• Stanislawczyk , A. ; Forys , P. ; Sztangret , L. ; Barabasz , B. ; Kusiak , J. Optimization of production chains using the nature-inspired techniques . Steel Research International 2008 , 2 , 617 – 624 .
   Google Scholar
• Jurkovic , Z. ; Brezocnik , M. ; Grizelj , B. ; Mandic , V. Optimization of extrusion process by genetic algorithms and conventional techniques . Tehnicki Vjesnik-Technical Gazette 2009 , 16 , 27 – 33 .
   Web of Science ®Google Scholar
• Chedly , S. ; Chettah , A. ; Ichchou , M.N. Multiobjective optimization of molded LDPE foams characteristics using genetic algorithm . Journal of Applied Polymer Science 2009 , 114 , 358 – 368 .
   Web of Science ®Google Scholar
• Pettersson , F. ; Biswas , A. ; Sen , P.K. ; Saxén , H. ; Chakraborti , N. Analyzing leaching data for low-grade manganese ore using neural nets and multiobjective genetic algorithms . Materials and Manufacturing Processes 2009 , 24 , 320 – 330 .
   Web of Science ®Google Scholar
• Biswas , A. ; Maitre , O. ; Mondal , D.N. ; Das , S.K. ; Sen , P.K. ; Collet , P. ; Chakraborti , N. Data-driven multiobjective analysis of manganese leaching from low grade sources using genetic algorithms, genetic programming, and other allied strategies . Materials and Manufacturing Processes 2011 , 26 , 415 – 430 .
   Web of Science ®Google Scholar
• Chen , C.-C. ; Su , P.-L. ; Chiou , C.-B. ; Chiang , K.-T. Experimental investigation of designed parameters on dimension shrinkage of injection molded thin-wall part by integrated response surface methodology and genetic algorithm: A case study . Materials and Manufacturing Processes 2011 , 26 , 534 – 540 .
   Web of Science ®Google Scholar
• Kim , B. ; Kim , D. ; Han , D.-I. ; Lee , N.-I. Optimization of wavelet-filtered in-situ plasma etch data using neural network and genetic algorithm . Materials and Manufacturing Processes 2011 , 26 , 398 – 402 .
   Web of Science ®Google Scholar
• Mitra , T. ; Helle , M. ; Pettersson , F. ; Saxén , H. ; Chakraborti , N. Multiobjective optimization of top gas recycling conditions in the blast furnace by genetic algorithms . Materials and Manufacturing Processes 2011 , 26 , 475 – 480 .
   Web of Science ®Google Scholar
• Ramteke , M. ; Srinivasan , R. Large-scale refinery crude oil scheduling by integrating graph representation and genetic algorithm . Industrial and Engineering Chemistry Research 2012 , 51 , 5256 – 5272 .
   Web of Science ®Google Scholar
• Chakraborti , N. ; Kumar , B.S. ; Babu , V.S. ; Moitra , S. ; Mukhopadhyay , A. A new multi-objective genetic algorithm applied to hot-rolling process . Applied Mathematical Modelling 2008 , 32 , 1781 – 1789 .
   Web of Science ®Google Scholar
• Karakaya , S. ; Soykasap , Ö. Buckling optimization of laminated composite plates using genetic algorithm and generalized pattern search algorithm. Structural and Multidisciplinary Optimization 2009, 39, 477–486.
   Web of Science ®Google Scholar
• Sivapathasekaran , C. ; Mukherjee , S. ; Ray , A. ; Gupta , A. ; Sen , R. Artificial neural network modeling and geneticalgorithm based medium optimization for the improved production of marine biosurfactant . Bioresource Technology 2010 , 101 , 2884 – 2887 .
   PubMed Web of Science ®Google Scholar
• Agarwal , A. ; Tewary , U. ; Pettersson , F. ; Das , S. ; Saxén , H. ; Chakraborti , N. Analysing blast furnace data using evolutionary neural network and multiobjective genetic algorithms . Ironmaking and Steelmaking 2010 , 37 , 353 – 359 .
   Web of Science ®Google Scholar
• Kovačič , M. ; Šarler , B. Genetic algorithm-based batch filling scheduling in the steel industry . Materials and Manufacturing Processes 2011 , 26 , 464 – 474 .
   Web of Science ®Google Scholar
• Sharma , S. ; Chua , Y.C. ; Rangaiah , G.P. Economic and environmental criteria and trade-offs for recovery processes . Materials and Manufacturing Processes 2011 , 26 , 431 – 445 .
   Web of Science ®Google Scholar
• Schmidt , A. Numerical prediction and sequential process optimization in sheet forming based on genetic algorithm . Materials and Manufacturing Processes 2011 , 26 , 521 – 526 .
   Web of Science ®Google Scholar
• Mitra , K. Handling uncertainty in kinetic parameters in optimal operation of a polymerization reactor . Materials and Manufacturing Processes 2011 , 26 , 446 – 454 .
   Web of Science ®Google Scholar
• Li , Y. ; Tseng , Y.-H. Hybrid differential evolution and particle swarm optimization approach to surface-potential-based model parameter extraction for nanoscale MOSFETs . Materials and Manufacturing Processes 2011 , 26 , 388 – 397 .
   Web of Science ®Google Scholar
• Gujarathi , A.M. ; Babu , B.V. Multiobjective optimization of industrial processes using elitist multiobjective differential evolution (Elitist-MODE) . Materials and Manufacturing Processes 2011 , 26 , 455 – 463 .
   Web of Science ®Google Scholar
• Nandi , A.K. ; Datta , S. ; Deb , K. Investigating the role of nonmetallic fillers in particulate-reinforced mold composites using EAs . Materials and Manufacturing Processes 2011 , 26 , 541 – 549 .
   Web of Science ®Google Scholar
• Nandi , A.K. ; Deb , K. ; Ganguly , S. ; Datta , S. Investigating the role of metallic fillers in particulate reinforced flexible mould material composites using evolutionary algorithms . Applied Soft Computing 2012 , 12 , 28 – 39 .
   Web of Science ®Google Scholar
• Garshasbinia , N. ; Jam , J.E. Identification of mechanical properties in laminated composite plates using genetic algorithm . Polymer Journal 2005 , 14 , 39 – 46 .
   Web of Science ®Google Scholar
• Lakhdar , N. ; Djeffal , F. New optimized Dual-Material (DM) gate design to improve the submicron GaN-MESFETs reliability in subthreshold regime . Microelectronics Reliability 2012 , 52 , 958 – 963 .
   Web of Science ®Google Scholar
• Kesgin , U. Genetic algorithm and artificial neural network for engine optimisation of efficiency and NOx emission . Fuel 2004 , 83 , 885 – 895 .
   Web of Science ®Google Scholar
• Kosowski , K. ; Tucki , K. ; Kosowski , A. Turbine stage design aided by artificial intelligence methods . Expert Systems with Applications 2009 , 36 , 11536 – 11542 .
   Web of Science ®Google Scholar
• Dennis , B.H. ; Dulikravich , G.S. ; Egorov , I.N. ; Yoshimura , S. ; Herceg , D. Three-dimensional parametric shape optimization using parallel computers . Computational Fluid Dynamics Journal 2009 , 17 , 256 – 266 .
   Google Scholar
• Zhao , J.-Q. ; Wang , L. Center based genetic algorithm and its application to the stiffness equivalence of the aircraft wing . Expert Systems with Applications 2011 , 38 , 6254 – 6262 .
   Web of Science ®Google Scholar
• Wojcik , W. ; Kalita , M. ; Smolarz , A. Regulation of burning process in a single burner with NOx emission stabilisation . Przeglad Elektrotechniczny (Poland) 2010 , 86 , 40 – 42 .
   Web of Science ®Google Scholar
• Librantz , A.F.H. ; Coppini , N.L. ; Baptista , E.A. ; de Araújo , S.A. ; de Fátima Castello Rosa , A. Genetic algorithm applied to investigate cutting process parameters influence on workpiece price formation . Materials and Manufacturing Processes 2011 , 26 , 550 – 557 .
   Web of Science ®Google Scholar
• Pemha , E. ; Ngo Nyobe , E. Genetic algorithm approach and experimental confirmation of a laser-based diagnostic technique for the local thermal turbulence in a hot wind tunnel jet . Progress in Electromagnetics Research B 2011 , 28 , 325 – 350 .
   Google Scholar
• Pandey , A.K. ; Dubey , A.K. Simultaneous optimization of multiple quality characteristics in laser cutting of titanium alloy sheet . Optics and Laser Technology Optics and Laser Technology 2012 , 44 , 1858 – 1865 .
   Web of Science ®Google Scholar
• Jantos , P. ; Grzechca , D. ; Rutkowski , J. Evolutionary algorithms for global parametric fault diagnosis in analogue integrated circuits . Bulletin of the Polish Academy of Sciences-Technical Sciences 2012 , 60 , 133 – 142 .
   Web of Science ®Google Scholar
• Zhang , P. ; Xu , Z. ; Du , F. Optimizing casting parameters of ingot based on neural network and genetic algorithm. Proc. Fourth International Conference on Natural Computation IEEE, Jinan, China, August 25–27, 2008; 545–548.
   Google Scholar
• Govindan , D. ; Chakraborty , S. ; Chakraborti , N. Analyzing the fluid flow in continuous casting through evolutionary neural nets and multi-objective genetic algorithms . Steel Research International 2010 , 81 , 197 – 203 .
   Web of Science ®Google Scholar
• Sindhya , K. ; Miettinen , K. New perspective to continuous casting of steel with a hybrid evolutionary multiobjective algorithm . Materials and Manufacturing Processes 2011 , 26 , 481 – 492 .
   Web of Science ®Google Scholar
• Kotas , P. ; Tutum , C.C. ; Thorborg , J. ; Hattel , J.H. Elimination of hot tears in steel castings by means of solidification pattern optimization . Metallurgical and Materials Transactions B–Process Metallurgy and Materials Processing Science 2012 , 43 , 609 – 626 .
   Web of Science ®Google Scholar
• Gwiazda , T.D. Genetic Algorithms Reference. Volume II Mutation Operator for Numerical Optimization Problems ; Tomaszgwiazda ebooks : Łomianki , 2007 .
   Google Scholar
• Chaparro , B.M. ; Thuillier , S. ; Menezes , L.F. ; Manach , P.Y. ; Fernandes , J.V. Material parameters identification: Gradient-based, genetic and hybrid optimization algorithms . Computational Materials Science 2008 , 44 , 339 – 346 .
   Web of Science ®Google Scholar
• Rios , L.M. ; Sahinidis , N.V. Derivative-free optimization: A review of algorithms and comparison of software implementation. Journal of Global Optimization 2013, in press.
   Google Scholar
• Rios , L.M. Algorithms for Derivative-free Optimization. Ph.D. Dissertation. Univ. of Illinois at Urbana Champaign, 2009.
   Google Scholar
• Pham , N. ; Malinowski , A. ; Bartczak , T. Comparative study of derivative free optimization algorithms . IEEE Transactions on Industrial Informatics 2011 , 7 , 592 – 600 .
   Web of Science ®Google Scholar
• Zang , H. ; Zhang , S. ; Hapeshi , K. A review of nature-inspired algorithms . Journal of Bionic Engineering 2010 , 7 , S232 – S237 .
   Web of Science ®Google Scholar
• Hartke , B. Global optimization . Wiley Interdisciplinary Reviews—Computational Molecular Science 2011 , 1 , 879 – 887 .
   Web of Science ®Google Scholar
• Deep , K. ; Barak , S. ; Katiyar , V.K. A new real coded genetic algorithm operator: Log logistic mutation. Proc. of the International Conference on Soft Computing for Problem Solving (SOCPROS 2011), December 20–22, 2011. Advances in Intelligent and Soft Computing 2012, 130, 193–200.
   Google Scholar
• Arabas , J. Approximating the genetic diversity of populations in the quasi-equilibrium state . IEEE Transactions on Evolutionary Computation 2012 , 16 , 632 – 644 .
   Web of Science ®Google Scholar
• Collet , P. ; Schoenauer , M. GUIDE: Unifying evolutionary engines through a graphical user interface. in: Evolution Artificielle. 6th International Conference, Marseilles, France, Oct. 27–30, 2003. Liardet, P.; Collet, P.; Fonlupt, C.; Lutton, E.; Schoenauer, M. (eds.) Lecture Notes in Computer Science 2004, 2936, 203–215 .
   Google Scholar
• Moral , R.F. ; Dulikravich , G.S. Multi-objective hybrid evolutionary optimization with automatic switching among constituent algorithms . The American Institute of Aeronautics and Astronautics Journal 2008 , 46 , 673 – 681 .
   Google Scholar
• Wishart , D.S. ; Arndt , D. ; Berjanskii , M. ; Tang , P. ; Zhou , J. ; Lin , G. CS23D: A web server for rapid protein structure generation using NMR chemical shifts and sequence data . Nucleic Acids Research 2008 , 36 , W496 – W502 .
   PubMed Web of Science ®Google Scholar
• Lonie , D.C. ; Zurek , E. XTALOPT: An open-source evolutionary algorithm for crystal structure prediction . Computer Physics Communications 2011 , 182 , 372 – 387 .
   Web of Science ®Google Scholar
• Maitre , O. ; Baumes , L.A. ; Lachiche , N. ; Corma , A. ; Collet , P. Coarse grain parellelization of evolutionary algorithms on GPGPU cards with EASEA Proc. GECCO '09 (11th Annual conference on Genetic and evolutionary computation). ACM: New York, NY, USA, 2009, pp. 1403–1410.
   Google Scholar
• Baumes , L.A. ; Krüger , F. ; Jimenez , S. ; Collet , P. ; Corma , A. Boosting theoretical zeolitic framework generation for the determination of new materials structures using GPU programming . Physical Chemistry Chemical Physics 2011 , 13 , 4674 – 4678 .
   PubMed Web of Science ®Google Scholar
• Maitre , O. ; Krüger , F. ; Querry , S. ; Lachiche , N. ; Collet , P. EASEA: Specification and execution of evolutionary algorithms on GPGPU . Soft Computing 2012 , 16 , 261 – 279 .
   Web of Science ®Google Scholar
• Collet , P. ; Maitre , O. ; Krüger , F. GECCO 2012 GPGPU competition. The EASEA parallelization platform. Proc. GECCO 2012, in press.
   Google Scholar
• http://www.gpgpgpu.com/
   Google Scholar
• Arabas , J. Evolutionary computation for global optimization: Current trends . Journal of Telecommunications and Information Technology 2011 , 4 , 5 – 10 .
   Google Scholar