[1] Ken A Dill and Justin L MacCallum. The protein-folding problem, 50 years on. Science, 338(6110):1042–1046, 2012.
[2] Lawrence A Kelley and Michael JE Sternberg. Protein structure prediction on the web: a case study using the phyre server. Nature protocols, 4(3):363, 2009.
[3] Ambrish Roy, Alper Kucukural, and Yang Zhang. I-tasser: a unified plat- form for automated protein structure and function prediction. Nature protocols, 5(4):725, 2010.
[4] Yang Zhang. Progress and challenges in protein structure prediction. Current opinion in structural biology, 18(3):342–348, 2008.
[5] Christian B. Anfinsen. Principles that govern the folding of protein chains. Science, 181(4096):223–230, 1973.
[6] UniProt Consortium et al. Uniprot: the universal protein knowledgebase. Nu- cleic Acids Research, 45(D1):D158–D169, 2017.
[7] Roshni Bhattacharya, Peter W Rose, Stephen K Burley, and Andreas Prli´c. Impact of genetic variation on three dimensional structure and function of pro- teins. PloS One, 12(3):e0171355, 2017.
[8] Jeffrey Skolnick and Jacquelyn S Fetrow. From genes to protein structure and function: novel applications of computational approaches in the genomic era. Trends in biotechnology, 18(1):34–39, 2000.
[9] John Moult, Krzysztof Fidelis, Andriy Kryshtafovych, Torsten Schwede, and Anna Tramontano. Critical assessment of methods of protein structure predic- tion: Progress and new directions in round xi. Proteins: Structure, Function, and Bioinformatics, 84(S1):4–14, 2016.
[10] Marco Biasini, Stefan Bienert, Andrew Waterhouse, Konstantin Arnold, Gabriel Studer, Tobias Schmidt, Florian Kiefer, Tiziano Gallo Cassarino, Martino Bertoni, Lorenza Bordoli, et al. Swiss-model: modelling protein tertiary and quaternary structure using evolutionary information. Nucleic Acids Research, page gku340, 2014.
[11] Jianyi Yang, Renxiang Yan, Ambrish Roy, Dong Xu, Jonathan Poisson, and Yang Zhang. The i-tasser suite: protein structure and function prediction. Nature Methods, 12(1):7–8, 2015.
[12] Arunachalam Jothi. Principles, challenges and advances in ab initio protein structure prediction. Protein and peptide letters, 19(11):1194–1204, 2012.
[13] Kristian W Kaufmann, Gordon H Lemmon, Samuel L DeLuca, Jonathan H Sheehan, and Jens Meiler. Practically useful: what the rosetta protein modeling suite can do for you. Biochemistry, 49(14):2987–2998, 2010.
[14] Dong Xu and Yang Zhang. Ab initio protein structure assembly using contin- uous structure fragments and optimized knowledge-based force field. Proteins: Structure, Function, and Bioinformatics, 80(7):1715–1735, 2012.
[15] Richard Bonneau and David Baker. Ab initio protein structure prediction: progress and prospects. Annual review of biophysics and biomolecular structure, 30(1):173–189, 2001.
[16] Corey Hardin, Taras V Pogorelov, and Zaida Luthey-Schulten. Ab initio protein structure prediction. Current opinion in structural biology, 12(2):176–181, 2002.
[17] Michael Feig. Computational protein structure refinement: almost there, yet still so far to go. Wiley Interdisciplinary Reviews: Computational Molecular Science, 7(3), 2017.
[18] Md Kamrul Islam and Madhu Chetty. Clustered memetic algorithm with local heuristics for ab initio protein structure prediction. IEEE Transactions on Evolutionary Computation, 17(4):558–576, 2013.
[19] David Baker and Andrej Sali. Protein structure prediction and structural ge- nomics. Science, 294(5540):93–96, 2001.
[20] Lisa N Kinch, Wenlin Li, Bohdan Monastyrskyy, Andriy Kryshtafovych, and Nick V Grishin. Evaluation of free modeling targets in casp11 and roll. Proteins: Structure, Function, and Bioinformatics, 84(S1):51–66, 2016.
[21] Kalyanmoy Deb. Multi-objective optimization using evolutionary algorithms, volume 16. John Wiley & Sons, 2001.
[22] Jiahai Wang, Weiwei Zhang, and Jun Zhang. Cooperative differential evolution with multiple populations for multiobjective optimization. IEEE Transactions on Cybernetics, 46(12):2848–2861, 2016.
[23] Jiahai Wang, Ying Zhou, Yong Wang, Jun Zhang, CL Philip Chen, and Zibin Zheng. Multiobjective vehicle routing problems with simultaneous delivery and pickup and time windows: formulation, instances, and algorithms. IEEE Trans- actions on Cybernetics, 46(3):582–594, 2016.
[24] Jiahai Wang, Jianjun Liao, Ying Zhou, and Yiqiao Cai. Differential evolution enhanced with multiobjective sorting-based mutation operators. IEEE Trans- actions on Cybernetics, 44(12):2792–2805, 2014.
[25] Vincenzo Cutello, Giuseppe Narzisi, and Giuseppe Nicosia. Computational studies of peptide and protein structure prediction problems via multiobjective evolutionary algorithms. In Multiobjective Problem Solving from Nature, pages 93–114. Springer, 2008.
[26] Christiane Regina Soares Brasil, Alexandre Claudio Botazzo Delbem, and Fer- nando Lu´ıs Barroso da Silva. Multiobjective evolutionary algorithm with many tables for purely ab initio protein structure prediction. Journal of Computa- tional Chemistry, 34(20):1719–1734, 2013.
[27] Mohammad Reza Bonyadi and Zbigniew Michalewicz. Particle swarm optimiza- tion for single objective continuous space problems: a review, 2017.
[28] Shuangbao Song, Junkai Ji, Xingqian Chen, Shangce Gao, Zheng Tang, and Yuki Todo. Adoption of an improved pso to explore a compound multi- objective energy function in protein structure prediction. Applied Soft Com- puting, 72:539–551, 2018.
[29] Shangce Gao, Yirui Wang, Jiahai Wang, and JiuJun Cheng. Understanding dif- ferential evolution: A poisson law derived from population interaction network. Journal of computational science, 21:140–149, 2017.
[30] Junkai Ji, Shangce Gao, Shuaiqun Wang, Yajiao Tang, Hang Yu, and Yuki Todo. Self-adaptive gravitational search algorithm with a modified chaotic local search. IEEE Access, 5:17881–17895, 2017.
[31] Zhenyu Song, Shangce Gao, Yang Yu, Jian Sun, and Yuki Todo. Multiple chaos embedded gravitational search algorithm. IEICE Transactions on Information and Systems, 100(4):888–900, 2017.
[32] Yirui Wang, Yang Yu, Shangce Gao, Haiyu Pan, and Gang Yang. A hierarchical gravitational search algorithm with an effective gravitational constant. Swarm and Evolutionary Computation, 46:118–139, 2019.
[33] Junkai Ji, Shuangbao Song, Cheng Tang, Shangce Gao, Zheng Tang, and Yuki Todo. An artificial bee colony algorithm search guided by scale-free networks. Information Sciences, 473:142–165, 2019.
[34] Shi Wang, Shuangyu Song, Yang Yu, Zhe Xu, Hanaki Yachi, and Shangce Gao. Multiple chaotic cuckoo search algorithm. In International Conference on Swarm Intelligence, pages 531–542. Springer, 2017.
[35] Yang Yu, Shangce Gao, Shi Cheng, Yirui Wang, Shuangyu Song, and Fenggang Yuan. Cbso: a memetic brain storm optimization with chaotic local search. Memetic Computing, 10(4):353–367, 2018.
[36] Shangce Gao, Shuangbao Song, Jiujun Cheng, Yuki Todo, and Mengchu Zhou. Incorporation of solvent effect into multi-objective evolutionary algorithm for improved protein structure prediction. IEEE/ACM transactions on computa- tional biology and bioinformatics, 15(4):1365–1378, 2018.
[37] Shuangbao Song, Shangce Gao, Xingqian Chen, Dongbao Jia, Xiaoxiao Qian, and Yuki Todo. Aimoes: Archive information assisted multi-objective evolu- tionary strategy for ab initio protein structure prediction. Knowledge-Based Systems, 146:58–72, 2018.
[38] Zhenyu Song, Yajiao Tang, Xingqian Chen, Shuangbao Song, Shuangyu Song, and Shangce Gao. A preference-based multi-objective evolutionary strategy for ab initio prediction of proteins. In 2017 International Conference on Progress in Informatics and Computing (PIC), pages 7–12. IEEE, 2017.
[39] Junkai Ji, Shuangbao Song, Yajiao Tang, Shangce Gao, Zheng Tang, and Yuki Todo. Approximate logic neuron model trained by states of matter search algorithm. Knowledge-Based Systems, 163:120–130, 2019.
[40] Shangce Gao, MengChu Zhou, Yirui Wang, Jiujun Cheng, Hanaki Yachi, and Jiahai Wang. Dendritic neuron model with effective learning algorithms for classification, approximation, and prediction. IEEE transactions on neural net- works and learning systems, (99):1–14, 2018.
[41] Shuangyu Song, Xingqian Chen, Cheng Tang, Shuangbao Song, Zheng Tang, and Yuki Todo. Training an approximate logic dendritic neuron model using social learning particle swarm optimization algorithm. IEEE Access, 7:141947– 141959, 2019.
[42] Jun Chin Ang, Andri Mirzal, Habibollah Haron, and Haza Nuzly Abdull Hamed. Supervised, unsupervised, and semi-supervised feature selection: a review on gene selection. IEEE/ACM transactions on computational biology and bioinformatics, 13(5):971–989, 2016.
[43] Camila Silva de Magalh˜aes, Diogo Marinho Almeida, Helio Jos´e Correa Bar- bosa, and Laurent Emmanuel Dardenne. A dynamic niching genetic algorithm strategy for docking highly flexible ligands. Information Sciences, 289:206–224, 2014.
[44] Esteban L´opez-Camacho, Mar´ıa Jesu´s Garc´ıa Godoy, Jos´e Garc´ıa-Nieto, An- tonio J Nebro, and Jos´e F Aldana-Montes. Solving molecular flexible docking problems with metaheuristics: A comparative study. Applied Soft Computing, 28:379–393, 2015.
[45] Yang Zhang, Daisuke Kihara, and Jeffrey Skolnick. Local energy landscape flattening: parallel hyperbolic monte carlo sampling of protein folding. Proteins: Structure, Function, and Bioinformatics, 48(2):192–201, 2002.
[46] Juyong Lee, Jinhyuk Lee, Takeshi N Sasaki, Masaki Sasai, Chaok Seok, and Jooyoung Lee. De novo protein structure prediction by dynamic fragment as- sembly and conformational space annealing. Proteins: Structure, Function, and Bioinformatics, 79(8):2403–2417, 2011.
[47] Philip Bradley, Kira MS Misura, and David Baker. Toward high-resolution de novo structure prediction for small proteins. Science, 309(5742):1868–1871, 2005.
[48] Shangce Gao, Shuangbao Song, Jiujun Cheng, Yuki Todo, and MengChu Zhou. Incorporation of solvent effect into multi-objective evolutionary algorithm for improved protein structure prediction. IEEE/ACM Transactions on Computa- tional Biology and Bioinformatics, PP(99):1–1, 2017.
[49] Alexander D MacKerell Jr, Donald Bashford, MLDR Bellott, Roland Leslie Dunbrack Jr, Jeffrey D Evanseck, Martin J Field, Stefan Fischer, Jiali Gao, H Guo, Sookhee Ha, et al. All-atom empirical potential for molecular model- ing and dynamics studies of proteins. The journal of Physical Chemistry B, 102(18):3586–3616, 1998.
[50] Mark W Hauschild, Martin Pelikan, Kumara Sastry, and David E Goldberg. Using previous models to bias structural learning in the hierarchical boa. Evo- lutionary Computation, 20(1):135–160, 2012.
[51] Muhammad Iqbal, Will N Browne, and Mengjie Zhang. Reusing building blocks of extracted knowledge to solve complex, large-scale boolean problems. IEEE Transactions on Evolutionary Computation, 18(4):465–480, 2014.
[52] Sushil J Louis and John McDonnell. Learning with case-injected genetic algo- rithms. IEEE Transactions on Evolutionary Computation, 8(4):316–328, 2004.
[53] L. Feng, Y. S. Ong, S. Jiang, and A. Gupta. Autoencoding evolutionary search with learning across heterogeneous problems. IEEE Transactions on Evolution- ary Computation, PP(99):1–1, 2017.
[54] C-L Hwang and Abu Syed Md Masud. Multiple objective decision makingmeth- ods and applications: a state-of-the-art survey, volume 164. Springer Science & Business Media, 2012.
[55] Kalyanmoy Deb, Samir Agrawal, Amrit Pratap, and Tanaka Meyarivan. A fast elitist non-dominated sorting genetic algorithm for multi-objective opti- mization: Nsga-ii. In Parallel Problem Solving from Nature PPSN VI, pages 849–858. Springer, 2000.
[56] Carlos Segura, Carlos A Coello Coello, Gara Miranda, and Coromoto Le´on. Using multi-objective evolutionary algorithms for single-objective optimization. 4OR, 11(3):201–228, 2013.
[57] Bo Huang, Meng Chu Zhou, GongXuan Zhang, Ahmed Chiheb Ammari, Ahmed Alabdulwahab, and Ayman G Fayoumi. Lexicographic multiobjective integer programming for optimal and structurally minimal petri net supervisors of au- tomated manufacturing systems. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 45(11):1459–1470, 2015.
[58] Xingquan Zuo, Cheng Chen, Wei Tan, and Meng Chu Zhou. Vehicle scheduling of an urban bus line via an improved multiobjective genetic algorithm. IEEE Transactions on Intelligent Transportation Systems, 16(2):1030–1041, 2015.
[59] Darrell F Lochtefeld and Frank W Ciarallo. An analysis of decomposition approaches in multi-objectivization via segmentation. Applied Soft Computing, 18:209–222, 2014.
[60] Carlos Segura, Eduardo Segredo, and Coromoto Le´on. Scalability and robust- ness of parallel hyperheuristics applied to a multiobjectivised frequency assign- ment problem. Soft Computing, 17(6):1077–1093, 2013.
[61] Jiahai Wang, Ying Zhou, Yong Wang, Jun Zhang, CL Chen, and Zibin Zheng. Multiobjective vehicle routing problems with simultaneous delivery and pickup and time windows: formulation, instances, and algorithms. IEEE Transactions on Cybernetics, 46(3):582–594, 2015.
[62] Hai-Peng Ren, Xiao-Na Huang, and Jia-Xuan Hao. Finding robust adaptation gene regulatory networks using multi-objective genetic algorithm. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 13(3):571–577, 2016.
[63] Y. Zhang, D. Gong, and J. Cheng. Multi-objective particle swarm optimization approach for cost-based feature selection in classification. IEEE/ACM Trans- actions on Computational Biology and Bioinformatics, PP(99):1–1, 2015.
[64] Carol A Rohl, Charlie EM Strauss, Kira MS Misura, and David Baker. Protein structure prediction using rosetta. Methods in Enzymology, 383:66–93, 2004.
[65] Sitao Wu, Jeffrey Skolnick, and Yang Zhang. Ab initio modeling of small pro- teins by iterative tasser simulations. BMC Biology, 5(1):17, 2007.
[66] Glennie Helles. A comparative study of the reported performance of ab initio protein structure prediction algorithms. Journal of the Royal Society Interface, 5(21):387–396, 2008.
[67] Daniel WA Buchan, Federico Minneci, Tim CO Nugent, Kevin Bryson, and David T Jones. Scalable web services for the psipred protein analysis workbench. Nucleic Acids Research, 41(W1):W349–W357, 2013.
[68] Pierrick Craveur, Agnel Praveen Joseph, Pierre Poulain, Alexandre G de Brevern, and Joseph Rebehmed. Cis–trans isomerization of omega dihedrals in proteins. Amino Acids, 45(2):279–289, 2013.
[69] Roland L Dunbrack. Rotamer libraries in the 21 st century. Current Opinion in Structural Biology, 12(4):431–440, 2002.
[70] Jos´e C Calvo, Julio Ortega, and Mancia Anguita. Pitagoras-psp: Including do- main knowledge in a multi-objective approach for protein structure prediction. Neurocomputing, 74(16):2675–2682, 2011.
[71] Bruno Borguesan, Mariel Barbachan e Silva, Bruno Grisci, Mario Inostroza- Ponta, and M´arcio Dorn. Apl: An angle probability list to improve knowledge- based metaheuristics for the three-dimensional protein structure prediction. Computational Biology and Chemistry, 59:142–157, 2015.
[72] Wolfgang Kabsch. A solution for the best rotation to relate two sets of vectors. Acta Crystallographica Section A: Crystal Physics, Diffraction, Theoretical and General Crystallography, 32(5):922–923, 1976.
[73] Adam Zemla, Cˇeslovas Venclovas, John Moult, and Krzysztof Fidelis. Process- ing and evaluation of predictions in casp4. Proteins: Structure, Function, and Bioinformatics, 45(S5):13–21, 2001.
[74] Adam Zemla. Lga: a method for finding 3d similarities in protein structures. Nucleic acids research, 31(13):3370–3374, 2003.
[75] Cyrus Levinthal. Are there pathways for protein folding. J. Chim. phys, 65(1):44–45, 1968.
[76] Philip Bradley, Kira M. S. Misura, and David Baker. Toward high-resolution de novo structure prediction for small proteins. Science, 309(5742):1868–1871, 2005.
[77] Bonnie Berger and Tom Leighton. Protein folding in the hydrophobic- hydrophilic (hp) model is np-complete. Journal of Computational Biology, 5(1):27–40, 1998.
[78] William E Hart and Sorin Istrail. Robust proofs of np-hardness for protein fold- ing: general lattices and energy potentials. Journal of Computational Biology, 4(1):1–22, 1997.
[79] Jooyoung Lee, Sitao Wu, and Yang Zhang. Ab initio protein structure predic- tion, chapter 1, pages 3–25. Springer, 2009.
[80] Bernard R Brooks, Charles L Brooks, Alexander D MacKerell, Lennart Nilsson, Robert J Petrella, Benoˆıt Roux, Youngdo Won, Georgios Archontis, Christian Bartels, Stefan Boresch, et al. Charmm: the biomolecular simulation program. Journal of Computational Chemistry, 30(10):1545–1614, 2009.
[81] William L Jorgensen and Julian Tirado-Rives. The opls [optimized potentials for liquid simulations] potential functions for proteins, energy minimizations for crystals of cyclic peptides and crambin. Journal of the American Chemical Society, 110(6):1657–1666, 1988.
[82] Yuedong Yang and Yaoqi Zhou. Specific interactions for ab initio folding of pro- tein terminal regions with secondary structures. Proteins: Structure, Function, and Bioinformatics, 72(2):793–803, 2008.
[83] Jian Zhang and Yang Zhang. A novel side-chain orientation dependent potential derived from random-walk reference state for protein fold selection and structure prediction. PLoS One, 5(10):e15386, 2010.
[84] Joshua D Knowles, Richard A Watson, and David W Corne. Reducing lo- cal optima in single-objective problems by multi-objectivization. In Interna- tional Conference on Evolutionary Multi-Criterion Optimization, pages 269–283. Springer, 2001.
[85] Soo-Yong Shin, In-Hee Lee, Dongmin Kim, and Byoung-Tak Zhang. Multiob- jective evolutionary optimization of dna sequences for reliable dna computing. IEEE Transactions on Evolutionary Computation, 9(2):143–158, 2005.
[86] Silvia Curteanu, Florin Leon, and Dan Gˆalea. Alternatives for multiobjective optimization of a polymerization process. Journal of Applied Polymer Science, 100(5):3680–3695, 2006.
[87] Mehmet Kaya, Abdullah Sarhan, and Reda Alhajj. Multiple sequence alignment with affine gap by using multi-objective genetic algorithm. Computer Methods and Programs in Biomedicine, 114(1):38–49, 2014.
[88] CA Coello Coello. Evolutionary multi-objective optimization: a historical view of the field. IEEE Computational Intelligence Magazine, 1(1):28–36, 2006.
[89] Themis Lazaridis and Martin Karplus. Effective energy functions for protein structure prediction. Current Opinion in Structural Biology, 10(2):139–145, 2000.
[90] Wendy D Cornell, Piotr Cieplak, Christopher I Bayly, Ian R Gould, Kenneth M Merz, David M Ferguson, David C Spellmeyer, Thomas Fox, James W Caldwell, and Peter A Kollman. A second generation force field for the simulation of proteins, nucleic acids, and organic molecules j. am. chem. soc. 1995, 117, 5179- 5197. Journal of the American Chemical Society, 118(9):2309–2309, 1996.
[91] Bernard Brooks and Martin Karplus. Harmonic dynamics of proteins: normal modes and fluctuations in bovine pancreatic trypsin inhibitor. Proceedings of the National Academy of Sciences, 80(21):6571–6575, 1983.
[92] Vincenzo Cutello, Giuseppe Narzisi, and Giuseppe Nicosia. A multi-objective evolutionary approach to the protein structure prediction problem. Journal of The Royal Society Interface, 3(6):139–151, 2006.
[93] Sandra M Sc´os Venske, Richard A Gon¸calves, Elaine M Benelli, and Myriam R Delgado. A multiobjective algorithm for protein structure prediction using adaptive differential evolution. In Intelligent Systems (BRACIS), 2013 Brazilian Conference on, pages 263–268. IEEE, 2013.
[94] David Becerra, Angelica Sandoval, Daniel Restrepo-Montoya, and Luis Fer- nando Nino. A parallel multi-objective ab initio approach for protein structure prediction. In Bioinformatics and Biomedicine (BIBM), 2010 IEEE Interna- tional Conference on, pages 137–141. IEEE, 2010.
[95] MV Judy, KS Ravichandran, and K Murugesan. A multi-objective evolutionary algorithm for protein structure prediction with immune operators. Computer Methods in Biomechanics and Biomedical Engineering, 12(4):407–413, 2009.
[96] Byungkook Lee and Frederic M Richards. The interpretation of protein structures: estimation of static accessibility. Journal of Molecular Biology, 55(3):379IN3–400IN4, 1971.
[97] Eshel Faraggi, Tuo Zhang, Yuedong Yang, Lukasz Kurgan, and Yaoqi Zhou. Spine x: improving protein secondary structure prediction by multistep learning coupled with prediction of solvent accessible surface area and backbone torsion angles. Journal of Computational Chemistry, 33(3):259–267, 2012.
[98] Martin Karplus and Eugene Shakhnovich. Protein folding: theoretical studies of thermodynamics and dynamics. Protein Folding, pages 127–195, 1992.
[99] Themis Lazaridis, Georgios Archontis, and Martin Karplus. Enthalpic contribu- tion to protein stability: insights from atom-based calculations and statistical mechanics. Advances in Protein Chemistry, 47:231–306, 1995.
[100] William L Jorgensen, Jayaraman Chandrasekhar, Jeffry D Madura, Roger W Impey, and Michael L Klein. Comparison of simple potential functions for simulating liquid water. The Journal of Chemical Physics, 79(2):926–935, 1983.
[101] Themis Lazaridis and Martin Karplus. Effective energy function for proteins in solution. Proteins: Structure, Function, and Bioinformatics, 35(2):133–152, 1999.
[102] Sergio A Hassan, Ernest L Mehler, Daqun Zhang, and Harel Weinstein. Molecu- lar dynamics simulations of peptides and proteins with a continuum electrostatic model based on screened coulomb potentials. Proteins: Structure, Function, and Bioinformatics, 51(1):109–125, 2003.
[103] Laura Wesson and David Eisenberg. Atomic solvation parameters applied to molecular dynamics of proteins in solution. Protein Science, 1(2):227–235, 1992.
[104] David Eisenberg and Andrew D McLachlan. Solvation energy in protein folding and binding. Nature, 319(6050):199–203, 1985.
[105] Di Qiu, Peter S Shenkin, Frank P Hollinger, and W Clark Still. The gb/sa continuum model for solvation. a fast analytical method for the calculation of approximate born radii. The Journal of Physical Chemistry A, 101(16):3005– 3014, 1997.
[106] Andreas Klamt and GJGJ Schu¨u¨rmann. Cosmo: a new approach to dielectric screening in solvents with explicit expressions for the screening energy and its gradient. Journal of the Chemical Society, Perkin Transactions 2, 5:799–805, 1993.
[107] Christoph Hartlmu¨ller, Christoph G¨obl, and Tobias Madl. Prediction of protein structure using surface accessibility data. Angewandte Chemie, 128(39):12149– 12153, 2016.
[108] Ingo Rechenberg. Cybernetic solution path of an experimental problem. Royal Aircraft Establishment, page Library translation No. 1122, 1965.
[109] H-P Schwefel. Evolutionsstrategie und numerische Optimierung. Technische Universit¨at Berlin, 1975.
[110] Joshua Knowles and David Corne. The pareto archived evolution strategy: A new baseline algorithm for pareto multiobjective optimisation. In Evolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress on, volume 1, pages 98–105. IEEE, 1999.
[111] Joshua D Knowles and David W Corne. Approximating the nondominated front using the pareto archived evolution strategy. Evolutionary Computation, 8(2):149–172, 2000.
[112] Xin Yao, Yong Liu, and Guangming Lin. Evolutionary programming made faster. Evolutionary Computation, IEEE Transactions on, 3(2):82–102, 1999.
[113] Giovanni Stracquadanio and Giuseppe Nicosia. Computational energy-based redesign of robust proteins. Computers & Chemical Engineering, 35(3):464– 473, 2011.
[114] Michiel JL De Hoon, Seiya Imoto, John Nolan, and Satoru Miyano. Open source clustering software. Bioinformatics, 20(9):1453–1454, 2004.
[115] Fionn Murtagh. A survey of recent advances in hierarchical clustering algo- rithms. The computer journal, 26(4):354–359, 1983.
[116] Eckart Zitzler, Lothar Thiele, Marco Laumanns, Carlos M Fonseca, and Vi- viane Grunert Da Fonseca. Performance assessment of multiobjective optimiz- ers: an analysis and review. Evolutionary Computation, IEEE Transactions on, 7(2):117–132, 2003.
[117] Robin C Purshouse and Peter J Fleming. Conflict, harmony, and indepen- dence: Relationships in evolutionary multi-criterion optimisation. In Evolu- tionary multi-criterion optimization, pages 16–30. Springer, 2003.
[118] Sandra M Venske, Richard A Gon¸calves, Elaine M Benelli, and Myriam R Delgado. Ademo/d: An adaptive differential evolution for protein structure prediction problem. Expert Systems with Applications, 56:209–226, 2016.
[119] M´arcio Dorn, Luciana S Buriol, and Luis C Lamb. A hybrid genetic algorithm for the 3-d protein structure prediction problem using a path-relinking strategy. In Evolutionary Computation (CEC), 2011 IEEE Congress on, pages 2709– 2716. IEEE, 2011.
[120] M´arcio Dorn and Osmar Norberto de Souza. Cref: a central-residue-fragment- based method for predicting approximate 3-d polypeptides structures. In Pro- ceedings of the 2008 ACM symposium on Applied computing, pages 1261–1267. ACM, 2008.
[121] Giuseppe Nicosia and Giovanni Stracquadanio. Generalized pattern search and mesh adaptive direct search algorithms for protein structure prediction. In International Workshop on Algorithms in Bioinformatics, pages 183–193. Springer, 2007.
[122] Angelo Marcello Anile, Vincenzo Cutello, Giuseppe Narzisi, Giuseppe Nicosia, and Salvatore Spinella. Determination of protein structure and dynamics com- bining immune algorithms and pattern search methods. Natural Computing, 6(1):55–72, 2007.
[123] B Jayaram, Kumkum Bhushan, Sandhya R Shenoy, Pooja Narang, Surojit Bose, Praveen Agrawal, Debashish Sahu, and Vidhu Pandey. Bhageerath: an energy based web enabled computer software suite for limiting the search space of ter- tiary structures of small globular proteins. Nucleic Acids Research, 34(21):6195– 6204, 2006.
[124] Lee R Cooper, David W Corne, and M James C Crabbe. Use of a novel hill- climbing genetic algorithm in protein folding simulations. Computational Biol- ogy and Chemistry, 27(6):575–580, 2003.
[125] J David Schaffer. Multiple objective optimization with vector evaluated ge- netic algorithms. In Proceedings of the 1st International Conference on Genetic Algorithms, pages 93–100. L. Erlbaum Associates Inc., 1985.
[126] Kalyanmoy Deb, Amrit Pratap, Sameer Agarwal, and TAMT Meyarivan. A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Transactions on Evolutionary Computation, 6(2):182–197, 2002.
[127] Hans-Georg Beyer and Hans-Paul Schwefel. Evolution strategies. Natural Com- puting, 1(1):3–52, 2002.
[128] JL Klepeis, MJ Pieja, and CA Floudas. Hybrid global optimization algorithms for protein structure prediction: Alternating hybrids. Biophysical Journal, 84(2):869–882, 2003.
[129] Yang Zhang, Andrzej Kolinski, and Jeffrey Skolnick. Touchstone ii: a new ap- proach to ab initio protein structure prediction. Biophysical journal, 85(2):1145– 1164, 2003.
[130] Julian Lee, Seung-Yeon Kim, and Jooyoung Lee. Protein structure prediction based on fragment assembly and parameter optimization. Biophysical Chem- istry, 115(2):209–214, 2005.
[131] George Chikenji, Yoshimi Fujitsuka, and Shoji Takada. A reversible fragment assembly method for de novo protein structure prediction. The Journal of Chemical Physics, 119(13):6895–6903, 2003.
[132] Mandavilli Srinivas and Lalit M Patnaik. Genetic algorithms: A survey. Com- puter, 27(6):17–26, 1994.
[133] Samir W Mahfoud. Crowding and preselection revisited. Urbana, 51:61801, 1992.
[134] Xingyi Zhang, Ye Tian, Ran Cheng, and Yaochu Jin. A decision variable clustering-based evolutionary algorithm for large-scale many-objective opti- mization. IEEE Transactions on Evolutionary Computation, PP(99):1–1, 2017.
[135] Eckart Ziztler, Marco Laumanns, and Lothar Thiele. Spea2: Improving the strength pareto evolutionary algorithm for multiobjective optimization. Evolu- tionary Methods for Design, Optimization, and Control, pages 95–100, 2002.
[136] Andriy Kryshtafovych, Alessandro Barbato, Krzysztof Fidelis, Bohdan Monastyrskyy, Torsten Schwede, and Anna Tramontano. Assessment of the assessment: evaluation of the model quality estimates in casp10. Proteins: Structure, Function, and Bioinformatics, 82(S2):112–126, 2014.
[137] Christine A Orengo, AD Michie, S Jones, David T Jones, MB Swindells, and Janet M Thornton. Cath–a hierarchic classification of protein domain struc- tures. Structure, 5(8):1093–1109, 1997.
[138] Sandra M. Venske, Richard A. Gonalves, Elaine M. Benelli, and Myriam R. Delgado. Ademo/d: An adaptive differential evolution for protein structure prediction problem. Expert Systems with Applications, 56:209 – 226, 2016.
[139] Steven A Combs, Samuel L DeLuca, Stephanie H DeLuca, Gordon H Lemmon, David P Nannemann, Elizabeth D Nguyen, Jordan R Willis, Jonathan H Shee- han, and Jens Meiler. Small-molecule ligand docking into comparative models with rosetta. Nature Protocols, 8(7):1277–1298, 2013.
[140] Oliviero Carugo and S´andor Pongor. A normalized root-mean-spuare dis- tance for comparing protein three-dimensional structures. Protein Science, 10(7):1470–1473, 2001.