Test Data Algoritma Kromosom Pada Sidik Jari Menggunakan Jaringan Syaraf
DOI:
https://doi.org/10.54066/jpsi.v2i1.1550Keywords:
Neural Network Data Test, Fingerprint, Chromosome AlgorithmAbstract
Biometric features that can be used for identification include iris, voice, DNA and fingerprints. Fingerprints are the most widely used biometric feature because of their uniqueness, universality and stability. Fingerprint recognition can be grouped into two different forms of problems, namely verification and identification. Verification is comparing one fingerprint with another fingerprint. Meanwhile, identification is matching an input fingerprint with fingerprint data in the database. Thus, identification can be interpreted as an extension of verification carried out by comparing one fingerprint to many fingerprints. Identification is inherently more complex than verification. The problem increases as the number of fingerprint datasets increases, resulting in an increase in the time required for the identification process. However, there is a way to overcome this complexity, namely classification. Apart from that, evolutionary algorithm optimization can also be carried out. The Chromosome Algorithm is an improvement on the evolutionary algorithm with a separate local search process. The memetic or chromosomal algorithm is a simple algorithm with reliable performance that can provide accurate solutions to problems in the real world. The current challenge is with the increasing growth of datasets (more than 106) which include the process of clustering text analysis, molecular DNA simulation, feature selection, and forecasting, handling large-scale optimization such as complex simulations, data mining, quantum chemistry, spectroscopic analysis, geophysical analysis, drug discovery, and fingerprint recognition studies. Chromosome algorithms have proven to be very competitive in large-scale optimization because they are based on stochastic algorithms that do not require gradient information.
References
Agarwal, D. & Merugu, S. (2007). Predictive discrete latent factor models for largescale dyadic data, in: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’07, ACM, New York, NY, USA, 2007, pp. 26–35. Aguilar, J. & Colmenares, A. (1998). Resolution of pattern recognition problems using a hybrid genetic/random neural network learning algorithm. Pattern Analysis and Applications. 1 (1): 52–61. doi:10.1007/BF01238026.
Aickelin, U. (1998). Nurse rostering with genetic algorithms. Proceedings of young operational research conference 1998. Guildford, UK.
Alsmadi M.K. (2017). An efficient similarity measure for content based image retrieval using memetic algorithm. Egyp. Jour. Bas. App. Sci. (2017), http://dx.doi.org/10.1016/j.ejbas.2017.02.004
Ali, M.A. (2013). Algoritma Genetik Tabu Search dan Memetika Pada Permasalahan Penjadwalan. Seminar Nasional Teknologi Informasi dan Multimedia 2013.
Aranha, C. & Iba, H. (2009). The Memetic tree-based genetic algorithm and its application to Portfolio Optimization. Memetic Comp. (2009), vol. 1, no. 2. pp. 139–151 DOI 10.1007/s12293-009-0010-2
Areibi, S. & Yang, Z. (2004). Effective memetic algorithms for VLSI design automation = genetic algorithms + local search + multi-level clustering. Evolutionary Computation. MIT Press. 12 (3): 327–353. doi:10.1162/10636560417749471535560.
Armstrong, R., Gannon, D., Geist, A., Keahey, K., Kohn, S., McInnes, L., Parker,S. & Smolinski, B. (1999). Toward a common component architecture for high- performance scientific computing. in: Proceedings of the 8th IEEE International Symposium on High Performance Distributed Computing, pp. 115–124.
Assiroj, P., Hananto, A.L., Fauzi, A. & Warnars, H.L.H.S. (2018). High Performance Computing Implementation: A Survey. The 1st INAPR Conference 2018, Jakarta, Indonesia. IEEE, doi: 10.1109/INAPR.2018.8627040.
Assiroj, P., Warnars H.L.H.S., Kosala, R., Ranti, B., Supangat, S., Kistijantoro,
A.I. & Abdurrachman, E. (2019). The form of High performace computing: A survey.OP Conf. Ser.: Mater. Sci. Eng. 662 052002, https://doi.org/10.1088/1757- 899X/662/5/052002.
Augugliaro, A., Dusonchet, L. & Sanseverino, E. R. (1998). Service restoration in compensated distribution networks using a hybrid genetic algorithm. Electric Power Systems Research. 46 (1): 59–66. doi:10.1016/S0378-7796(98)00025-X.
Bäck, T., Fogel, D.B. & Michalewicz, Z. (1997). (Eds.), Handbook of Evolutionary Computation, IOP Publishing. Ltd., Bristol, UK.
Bahmann, S. & Kortus, J. (2013). EVO—Evolutionary algorithm for crystal structure prediction. Comput. Phys. Commun. 184 (6) (2013) 1618–1625.
Bai, L., Liang, J., Dang, C. & Cao, F. (2011). A novel attribute weighting algorithm for clustering high-dimensional categorical data, Pattern Recogn. 44 (12) (2011) 2843–2861.
Bao, Hu & Xiong. (2013). A PSO and pattern search based memetic algorithm for SVMs parameters optimization. Neurocomputing, vol. 117, pp. 98–106, 2013. DOI: 10.1016/j.neucom.2013.01.027
Barrientos, R.J., G´omez, J.I., Tenllado, C., Matias, M.P., Marin, M. (2011). kNN query processing in metric spaces using GPUs. In: Jeannot, E., Namyst, R., Roman, J. (eds.) Euro-Par 2011. LNCS, vol. 6852, pp. 380–392. Springer, Heidelberg (2011).
https://doi.org/10.1007/978-3-642-23400-2 35
Bereta, M(2019).Baldwin effect and Lamarckian evolution in a memetic algorithm for Euclidean Steiner tree problem. Memetic Comput., vol. 11, no. 1, pp. 35–
Springer, Heidelberg (2019).
Bhanu, B., Tan, X. (2001). A triplet based approach for indexing of fingerprint database for identification. In: Bigun, J., Smeraldi, F. (eds.) AVBPA 2001. LNCS, vol. 2091, pp. 205–210. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-45344-X
Bhatt, H.S., Bharawaj, S., Singh, R. & Vasta, M. (2012). Memetically Optimized MCWLD for Matching Sketches With Digital Face Images. IEEE Transactions On Information Forensics And Security, vol. 7, no. 5, pp. 1522–1535, 2012.
Blum, J., Le Dimet, F. X. & Navon, I. M. (2009). Data assimilation for geophysical fluids, in: P.G. Ciarlet (Ed.), Handbook of Numerical Analysis, Handbook of Numerical Analysis, vol. 14, Elsevier, 2009, pp. 385–441.
Botzheim, J., Toda, Y. & Kubota, N. (2012). Bacterial memetic algorithm for offline path planning of mobile robots. Memetic Computing, vol. 4 No. 1, pp. 73-86. Springer Intl. DOI: 10.1007/s12293-012-0076-0
Bozejko, W. & Wodecki, M. (2011). The methodology of parallel memetic algorithms designing, doi:10.5220/0003186006430648, in Proceeding of the 3rd International Conference on Agents and Artificial Intelligence (ICAART-2011), pages 643-648, SCITEPRESS (Science and Technology Publications, Lda).
Buck, A.R., Keller, J.M. & Skubic, M. (2013). A memetic algorithm for matching spatial configurations with the histograms of forces. IEEE Transactions on Evolutionary Computation. Vol. 17-4. Pp. 588-604. doi: 10.1109/TEVC.2012.2226889
Burke E. K., Gendreau M., Hyde M., Kendall G., Ochoa G., Özcan E. and Qu R. (2013). "Hyper-heuristics: A Survey of the State of the Art". Journal of the Operational Research Society. 64 (12): 1695–1724.
Burke, E. & Smith, A. (1999). A memetic algorithm to schedule planned maintenance for the national grid. Journal of Experimental Algorithmics. 4 (4): 1–13. doi:10.1145/347792.347801.
Cabido, R., Montemayor, A.S. & Pantrigo, J.J. (2012). High performance memetic algorithm particle filter for multiple object tracking on modern GPUs. Journal Soft Comput (2012) vol. 16, no. 2, pp. 217–230. DOI: 10.1007/s00500-011-0715-2.
Cao, K., Liu, E., Jain, A.K. (2014). Segmentation and enhancement of latent fingerprints: a coarse to fine ridgestructure dictionary. IEEE Trans. Pattern Anal. Mach. Intell. 36(9), 1847–1859 (2014).
Caponio, A. & Neri, F. (2012). Handbook of Memetic Algorithms, SCI 379, pp. 241–260. Springer-Verlag Berlin Heidelberg 2012, DOI: 10.1007/978-3-642-23247- 3_15.
Caponio, A., Cascella, G.L., Neri, F., Salvatore, N. & Sumner, M. (2007). A fast adaptive memetic algorithm for off-line and on-line control design of PMSM drivers, IEEE Trans. Syst. Man Cybern. — Part B, Special Issue Memetic Algorithms 37 (1) (2007) 28–41.
Cappelli, R., Ferrara, M., Maltoni, D. (2010). Minutia cylinder-code: a new representation and matching technique for fingerprint recognition. IEEE Trans. Pattern Anal. Mach. Intell. 32(12), 2128–2141 (2010).
Cappelli, R., Ferrara, M., Maltoni, D. (2011). Fingerprint indexing based on minutia cylinder-code. IEEE Trans. Pattern Anal. Mach. Intell. 33(5), 1051–1057 (2011)
Cappelli, R., Maio, D. (2004). The state of the art in fingerprint classification. In: Ratha, N., Bolle, R. (eds.) Automatic Fingerprint Recognition Systems, pp. 183–205. Springer, New York (2004). https://doi.org/10.1007/0-387-21685-5 9.
Carey, P. (2015). Supercomputers a Hidden Power Center of Silicon Valley, The San Jose Mercury News, May 7, 2015, http://www.mercurynews.com/business/ci_28071868/supercomputershiddenpowercenter-silicon-valley.
Ch´avez, E., Navarro, G. (2005). A compact space decomposition for effective metric indexing. Pattern Recogn. Lett. 26(9), 1363–1376 (2005)
Ch´avez, E., Navarro, G., Baeza-Yates, R., Marroqu´ın, J.L. (2001). Searching in metric spaces. ACM Comput. Surv. 33(3), 273–321 (2001).
Changchit, C. Chuchuen, C. (2018). Cloud computing: An examination of factors impacting users’ adoption. Journal of Computer Information Systems, Vol. 58, 1-9
Chen, L., Fujishiro I. & Nakajima, K. (2002). Parallel performance optimization of large-scale unstructured data visualization for the earth simulator, In: Proceedings of the Fourth Eurographics Workshop on Parallel Graphics and Visualization, EGPGV ’02, Aire-la-Ville, Switzerland, Switzerland, 2002. Eurographics Association, pp. 133–140.
Chen, X. S., Ong, Y. S., Lim, M. H. & Tan, K. C. (2011). "A Multi-Facet Survey on Memetic Computation". IEEE Transactions on Evolutionary Computation. 15 (5): 591–607. doi:10.1109/tevc.2011.2132725.
Chen, X. S., Ong, Y. S. & Lim, M. H. (2010). "Research Frontier: Memetic Computation - Past, Present & Future". IEEE Computational Intelligence Magazine. 5 (2): 24–36. doi:10.1109/mci.2010.936309.
Chi, Y & Liu, J. (2014). Learning large-scale fuzzy cognitive maps using a hybrid of memetic algorithm and neural network. IEEE International Conference on Fuzzy Systems, 1036-1040. doi: 10.1109/FUZZ-IEEE.2014.6891604
Coello, C. (2006). Evolutionary Algorithms: Basic Concepts and Applications in Biometrics, Synthesis and Analysis in Biometrics, World Scientific Publishing, pp 1-34, 2006.
Cordon, O., Herrera, H. & Lozano, M. (1995). A Classified review on the combination fuzzy logic- genetic algorithms bibliography, Tech. Report 95129, Department of Computer Science and AI, Universidad de Granada, Granada, Spain, 1995, Available at : http://decsai.ugr.s/~herrera/flga.html.
Costa, V. G, Barrientos, R.J., Marin, M., Bonacic, C. (2010) Scheduling metric- space queries processing on multi-core processors. In: 18th Euromicro Conference on Parallel, Distributed and Network-based Processing (PDP 2010), pp. 187–194. IEEE Computer Society, Pisa (2010)
Costa, D. (1995). An evolutionary tabu search algorithm and the NHL scheduling problem. Infor 33: 161–178.
Data from Top500.org, “The List: November 2015,” http://www.top500.org/lists/2015/11/
Datta, A. & Soundaralakshmi, S. (2003). Fast parallel algorithm for distance transform. IEEE Transactions on System, Man, Cybernetics A, System, Humans 33 (2003) 429–434.
Di Gesù, V., Lo Bosco, G., Millonzi, F. & Valenti, C. (2008). A memetic algorithm for binary image reconstruction. Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 4958 LNCS, pp. 384–395, 2008.
Dworak, K & Boryczka, U. (2012). Differential Cryptanalysis of Symmetric Block Ciphers Using Memetic Algorithms. ACIIDS: Asian Conference on Intelligent Information and Database Systems. 4th Asian Conference, 2012 Proceedings, Part II. Springer International Publishing. 2012. DOI: 10.1007/978-3-642-28490-8.
Dudley, J.T., Schadt, E., Sirota, M., Butte, A.J. & Ashley, E. (2010). Drug discovery in a multidimensional world: systems, patterns, and networks, J.Cardiovasc. Trans. Res. 3 (5) (2010) 438–447.
Ergun, H., Hertem, D.V., & Belmans, R. (2012). Transmission system topology optimization for large-scale offshore wind integration, IEEE Trans. Sust. Energy 3 (4) (2012) 908–917.
Femandez, E., Grana, M. & Cabello, J.R. (2004). An instantaneous memetic algorithm for illumination correction. Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753) pp. 1105–1110, 2004. DOI: 10.1109/CEC.2004.1330985.
Feng, L., Tan, A.H., Lim, M.H. & Jian, S.W. (2012). Band selection for hyperspectral images using probabilistic memetic algorithm. Journal Soft Computing, vol. 20, no. 12, pp. 4685-4693, 2012. DOI: 10.1007/s00500-014-1508-1
França, P., Mendes, A. & Moscato, P. (1999). Memetic algorithms to minimize tardiness on a single machine with sequence-dependent setup times. Proceedings of the 5th International Conference of the Decision Sciences Institute. Athens, Greece. pp. 1708–1710.
Galar, M., Derrac, J., Peralta, D., Triguero, I., Paternain, D., Lopez-Molina, C., Garc´ıa, S.J., Benitez, M., Pagola, M. Barrenechea, E., Bustince, H. & Herrera, F. (2015). A survey of fingerprint classification Part I: Taxonomies on feature extraction methods and learning models, Knowledge-Based Systems 81 (2015) 76–97.
Galinier, P., Boujbel, Z. & Fernandes, M. C. (2011). An efficient memetic algorithm for the graph partitioning problem. Ann. Oper. Res., vol. 191, no. 1, pp. 1–22, 2011. DOI: 10.1007/s10479-011-0983-3.
Gálvez, A. & Iglesias, A. (2018). Modified Memetic Self-Adaptive Firefly Algorithm for 2D Fractal Image Reconstruction. 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC), Tokyo, 2018, pp. 165-170. doi: 10.1109/COMPSAC.2018.10222.
Ghosh, M., Kundu, T., Ghosh, D., & Sarkar, R. (2019). Feature selection for facial emotion recognition using late hill-climbing based memetic algorithm. Multimed. Tools Appl. doi:10.1007/s11042-019-07811-x.
Ghosh, M., Malakar, S., Bhowmik, S., Sarkar, R. & Nasipuri, M. (2017). Memetic Algorithm Based Feature Selection for Handwritten City Name Recognition. CICBA 2017, Part II, CCIS 776, pp. 599–613. Springer Nature Singapore Pte Ltd, 2017. DOI: 10.1007/978-981-10-6427-2.
Ghosh, M., Malakar, S., Bhowmik, S., Sarkar, R. & Nasipuri, M. (2005). Featue Selection for Handwriting Word Recognition Using Memetic Algorithm. Advances in Intelligent Computing, vol. 3644, no. November. Springer Singapore, 2005. doi: 10.1007/11538059.
Goldberg, E. (1989) Genetic Algorithms in Search, Optimization, and Machine Learning. Reading, MA: Addison-Wesley, 1989.
Gong, Y.J., Ge, Y.F., Li, J.J., Zhang, J. & Ip. W.H. (2016). A splicing-driven memetic algorithm for reconstructing cross-cut shredded text documents. Appl. Soft Comput. J., vol. 45, pp. 163–172, 2016. DOI: 10.1016/j.asoc.2016.03.024.
Haas, O., Burnham, K. & Mills, J. (1998). Optimization of beam orientation in radiotherapy using planar geometry. Physics in Medicine and Biology. 43 (8): 2179– 2193. doi:10.1088/0031-9155/43/8/013. PMID 9725597.
Haque, M.N., Mathieson, L & Moscato, P. (2018). A memetic algorithm for community detection by maximising the connected cohesion. 2017 IEEE Symp. Ser. Comput. Intell. SSCI 2017 - Proc., vol. 2018-Janua, pp. 1–8, 2018.
Harmon, A. (2014). HPC Matters: Funding, Collaboration, Innovation. ScienceNode, November 26, 2014, https://sciencenode.org/feature/hpc-matters-funding- collaboration-innovation.php.
Harris, S. & Ifeachor, E. (1998). Automatic design of frequency sampling filters by hybrid genetic algorithm techniques. IEEE Transactions on Signal Processing. 46(12): 3304–3314. DOI:10.1109/78.735305.
Hart, W., Krasnogor, N. & Smith, J.E. (2005). Memetic evolutionary algorithms, pp. 3–27 (2005).
Hart W. (1994). Adaptive global optimization with local search. Ph. D. Thesis, University of California, San Diego
Holland, J. (1975). Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor, MI, 1975.
Hong, J.H., Min, J.K., Cho, U.K. & Cho, S.B. (2008). Fingerprint classification using onevs-all support vector machines dynamically ordered with Na¨ı ve Bayes classifiers. Pattern Recogn. 41(2), 662–671 (2008).
Hong, J.H. & Cho, S.B. (2006). Efficient huge-scale feature selection with speciated genetic algorithm, Pattern Recogn. Lett. 27 (2) (2006) 143–150.
Huang, K.W., Wu, Z.X., Peng, H.W., Tsai, M.C., Hung, Y.C. & Lu, Y.C. (2018).
A memetic particle gravitation optimization algorithm for solving image segmentation. Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018. pp. 82-85. DOI: 10.1109/ICASI.2018.8394392.
Huang, K.W., Wu, Z.X., Peng, H.W., Tsai, M.C., Hung, Y.C. & Lu, Y.C. (2019).
Memetic particle gravitation optimization algorithm for solving clustering problems.
IEEE Access, vol. 7, pp. 80950-80968, 2019. DOI: 10.1109/ACCESS.2019.2923979
Hwang, B.W., Kim, S., Lee, S.W., Huang, D.S., Zhang, X.P., & Huang, G.B. (2005). Feature Selection for Handwritten Word Recognition Using Memetic Algorithm. Advances in Intelligent Computing, vol. 3644, no. November. Springer Singapore, 2005.IBM White Papers. (2015). https://www-03.ibm.com/support/techdocs/atsmastr.nsf/Web/WhitePapers (diakses 10 Januari 2020).
Ibrahiem, E.E. & El-Kareem, M. (2008). On the application of Genetic Algorithm in Finger Print Recognition, World Applied Sciences Journal, 5(3), pp 276-281, 2008.
Ichimura, T. & Kuriyama, Y. (1998). Learning of neural networks with parallel hybrid GA using a royal road function. IEEE International Joint Conference on Neural Networks. 2. New York, NY. pp. 1131–1136.
IDC. High Performance Computing in the EU: Progress on the Implementation of the European HPC Strategy (Brussels: European Commission DG Communications Networks, Content & Technology, 2015), 17, http://knowledgebase.eirg.eu/documents/243153/246094/High+Performance+Computin g+in+the+EU+Progress+on+the+Implementation+of+the+European+HPC+Strategy.pdf
/b0adf617-3f50-4a6f-9217- 4e0fbb5edd09.
Jain, A.K., Bolle, R.M., Pankanti, S. (2005). Biometrics: Personal Identification in Networked Society. Springer International.
Jain, A.K., Feng, J. (2011). Latent fingerprint matching, IEEE Transactions on Pattern Analysis and Machine Intelligence 33, 88–100.
Jain, A.K, Flynn, P., Ross, A.A. (2007). Handbook of Biometrics. Springer, New York (2007). https://doi.org/10.1007/978-0-387-71041-9.
Jain, A.K., Hong, L., & Bolle, R. (1997). On-line fingerprint verification, IEEE Transactions on Pattern Analysis and Machine Intelligence 19 (1997) 302–314.
Jain, A.K., Hong, L., Pankanti, S. & Bolle, R. (1997). An identity-authentication system using fingerprints, Proceedings of IEEE 85 (1997) 1365–1388.
Jayaram, M.A. & Fleyeh, H. (2013). Soft Computing in Biometrics: A Pragmatic Appraisal, American Journal of Intelligent Systems 2013, 3(3): 105-112. doi: 10.5923/j.ajis.20130303.01.
Jiao, L., Gong, M., Wang, S., Hou, B., Zheng, Z. & Wu, Q. (2010). Natural and remote sensing image segmentation using memetic computing, IEEE Comput. Intell. Mag. 5 (2) (2010) 78–91.
Jat, S.N & Yang, S. (2011). A Memetic Algorithm for the University Course Timetabling Problem. Systems Man and Cybernetics Part C: Applications and Reviews IEEE Transactions on, vol. 41, pp. 93-106, ISSN 1094-6977.
Johnston, D. (2014). HPC Matters to Our Quality of Life and Prosperity. Scientific Computing, November 11, 2014, http://www.scientificcomputing.com/articles/2014/11/hpc-matters-our-quality-life-andprosperity
Karkavitsas, G. & Tsihrintzis, G. (2011). "Automatic Music Genre Classification Using Hybrid Genetic Algorithms". Intelligent Interactive Multimedia Systems and Services. Springer. 11: 323–335. doi:10.1007/978-3-642-22158-3_32.
Kendall G., Soubeiga E. & Cowling P. (2002). Choice function and random hyperheuristics. 4th Asia-Pacific Conference on Simulated Evolution and Learning. SEAL 2002. pp. 667–671.
Kielarova, S.W. (2017). Development of Hybrid Memetic Algorithm and General Regression Neural Network for Generating Iterated Function System Fractals in Jewelry Design Applications. Proceedings, Part II, 8th International Conference, ICSI 2017, Fukuoka, Japan, July 27 – August 1, 2017, vol. 10386, pp. 280–289, 2017. Springer International. DOI: 10.1007/978-3-319-41000-5_28
Kollen, A. & Pesch, E. (1994). Genetic local search in combinatorial optimization, Discrete Applied Mathematics and Combinatorial Operation Research and Computer Science 48 (1994) 273– 284.
Krasnogor N. & Gustafson S. (2002). Toward truly "memetic" memetic algorithms: discussion and proof of concepts. Advances in Nature-Inspired Computation: the PPSN VII Workshops. PEDAL (Parallel Emergent and Distributed Architectures Lab). University of Reading.horizontal tab character in |journal= at position 138 (help)
Krasnogor N. (1999). "Coevolution of genes and memes in memetic algorithms".Graduate Student Workshop: 371.
Kumar, B.V., Karpagam, G. R. & Zhao, Y. (2019). Evolutionary Algorithm With Memetic Search Capability for Optic Disc Localization in Retinal Fundus Images, no. 1. Elsevier Inc., 2019.
Kumar, D., Kumar, S. & Rai, S. (2009). Feature Selection for face recognition: A Memetic algorithm approach. Journal of Zhejanga University Science, 10(8), pp 1140- 1152, 2009.
Kumar, A. & Kwong, C. (2013). Towards contactless, low-cost and accurate 3D fingerprint identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3438–3443 (2013).
Lang, S., Drouvelis, P., Tafaj, E., Bastian, P. & Sakmann, B. (2011). Fast extraction of neuron morphologies from large-scale SBFSEM image stacks, J. Comput. Neurosci. 31 (3) (2011) 533–545.
Lastra, M., Molinab D. & Benítez, J. M. (2015). A high performance memetic algorithm for extremely high-dimensional problems, Information Science 293 (2015) 35- 58, Elsevier Inc. http://dx.doi.org/10.1016/j.ins.2014.09.018.
Le, H.H., Nguyen, N.H., Nguyen, T.T. (2016). Exploiting GPU for large scale fingerprint identification. In: Nguyen, N.T., Trawi´ nski, B., Fujita, H., Hong, T.-P. (eds.) ACIIDS 2016. LNCS (LNAI), vol. 9621, pp. 688–697. Springer, Heidelberg (2016).
https://doi.org/10.1007/978-3-662-49381-6 66.
Li, Y., Hu, J., & Jia, Y. (2014) “Automatic SAR image enhancement based on nonsubsampled contourlet transform and memetic algorithm,” Neurocomputing, vol. 134, pp. 70–78, 2014. DOI: 10.1016/j.neucom.2013.03.068.
Liang, B., Liu, B., Zhou, F., Guo, B., Xu, X., Kang, J., Li, J. & Liu, W. (2015). A
Lin, J., & Chen, Y. (2011). Analysis on the collaboration between global search and local search in memetic computation, October 15(5) (2011) 608–623.
Lu, Y., Wang, S., Li, S. & Zhou, C. (2009). Particle swarm optimizer for variable weighting in clustering high-dimensional data, Mach. Learn. 82 (1) (2009) 43–70.
Madhavi KV, Tamilkodi R, & Sudha KJ. (2016). An innovative method for retrieving relevant images by getting the top-ranked images first using interactive genetic algorithm.Proc Comput Sci 2016;79:254–61. https://doi.org/10.1016/j.procs.2016.03.033
Maltoni, D., Maio, D., Jain, A.K. & Prabhakar, S. (2009). Handbook of Fingerprint Recognition, Springer-Verlag New York Inc.
Marksteiner, P. (1996) High-performance computing- an overview. Vienna University Computer Center Universithtsstrafle 7, A-1010 Vienna, Austria. Computer Physics Communications 97 16-35.
Manacher, G.K. (1967). Production and stabilization of real time task schedules. Journal of ACM 14 (1967) 439–465
Marin, M., Costa, V. G., Bonacic, C., Yates, R. B., Scherson, I.D. (2010). Sync/async parallel search for the efficient design and construction of web search engines. Parallel Comput. 36(4), 153–168 (2010).
Matsui, T., Katagiri, Y., Katagiri, H. & Kato, K. (2015). “Automatic feature point selection through hybrid metaheauristics based on tabu search and memetic algorithm for augmented reality,” Procedia Comput. Sci., vol. 60, no. 1, pp. 1120–1127, 2015.
Merz, P & Freisleben, B. (1999). Fitness Landscapes and Memetic Algorithm Design, in: D. Corne, M. Dorigo, F. Glower (Eds.), McGraw-Hill, London, 1999.
Merz, P. & Zell, A. (2002). Clustering Gene Expression Profiles with Memetic Algorithms. Parallel Problem Solving from Nature — PPSN VII. Springer. pp. 811–820. doi:10.1007/3-540-45712-7_78.
Montazeri, M & Iran, K. (2019). Memetic Algorithm Image Enhancement for Preserving Mean Brightness Without Losing Image Features. International Journal of Image and Graphics. 19. 1950020. 10.1142/S0219467819500207.
Moscato, P., Mendes, A. & Berretta, R. (2007). Benchmarking a memetic algorithm for ordering microarray data. BioSystems, vol. 88, no. 1–2, pp. 56–75, 2007. Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.biosystems.2006.04.005
Moscato, P. (1989). On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts: Toward Memetic Algorithms. Technical Report, Caltech Concurrent Computation Program, California Institute of Technology, Pasaden, 1989.
Moscato, P. (1999). Memetic Algorithms: a Short Introduction, in: D. Corne, M. Dorigo, F. Glower (Eds.), McGraw-Hill, London, 1999.
Mu, C., Xie, J., Liu, R. & Jiao, L. 2014. A memetic algorithm using local structural information for detecting community structure in complex networks. Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014. 680- 686. doi: 10.1109/CEC.2014.6900336
Nagy, B. & Moisi, E.V. (2016). Memetic algorithms for reconstruction of binary images on triangular grids with 3 and 6 projections, Appl. Soft Comput. J. (2016), http://dx.doi.org/10.1016/j.asoc.2016.10.014.
Navarro, G., Paredes, R.U., (2011). Fully dynamic metric access methods based on hyperplane partitioning. Inf. Syst. 36(4), 734–747 (2011).
Naveen, N & Rao, M.C. (2016). Bankruptcy Prediction Using Memetic Algorithm. 10th International Workshop, MIWAI 2016, Chiang Mai, Thailand, December 7-9, 2016, Proceedings, pp. 153-161. Springer Cham. https://doi.org/10.1007/978-3-319-49397-8.
Neri, F. & Mininno, E. (2010). Memetic compact differential evolution for cartessian robot control. IEEE Computational Intelligence Magazine 5(2), 54–65 (2010).
Nguyen K., Lu T., Le T. & Tran N. (2011) Memetic Algorithm for a University Course Timetabling Problem. In: Tan H. (eds) Informatics in Control, Automation and Robotics. Lecture Notes in Electrical Engineering, vol 132. Springer, Berlin, Heidelberg. DOI: https://doi.org/10.1007/978-3-642-25899-2_10
Niu, D., Wang, Y. & Wu, D. D. (2010). Power load forecasting using support vector machine and ant colony optimization, Expert Syst. Appl. 37 (3) (2010) 2531–2539.
Ong, Y.S., Lim, M.H., Chen, X. (2010). Memetic computation-past, present and future. IEEE Computational Intelligence Magazine 5(2), 24–31 (2010).
Ong, Y.S., Lim, M.H., Zhu N, & Wong, K.W. (2006). Classification of adaptive memetic algorithms: a comparative study, IEEE Trans. Syst. Man. Cybern. 36 (1) (2006) 141–152.
Ozcan, E. & Basaran, C. (2009). A Case Study of Memetic Algorithms for Constraint Optimization. Soft Computing: A Fusion of Foundations, Methodologies and Applications. 13(8–9): 871–882. DOI:10.1007/s00500-008-0354-4.
Ozcan, E. (2007). Memes, Self-generation and Nurse Rostering. Lecture Notes in Computer Science. Lecture Notes in Computer Science. Springer-Verlag. 3867: 85–104. DOI:10.1007/978-3-540-77345-0_6. ISBN 978-3-540-77344-3.
Ozcan, E. & Onbasioglu, E. (2006). Memetic Algorithms for Parallel Code Optimization. International Journal of Parallel Programming. 35 (1): 33–61. DOI:10.1007/s10766-006-0026-x.
Ozcan E., Mohan C.K. (1998) Steady state memetic algorithm for partial shape matching. In: Porto V.W., Saravanan N., Waagen D., Eiben A.E. (eds) Evolutionary Programming VII. EP 1998. Lecture Notes in Computer Science, vol 1447, pp. 527-536 Springer, Berlin, Heidelberg. DOI: https://doi.org/10.1007/BFb0040804
Pagacz, A & Hu, B. (2010). A Memetic Algorithm with population management for the generalized minimum vertex-biconnected network problem. Proc. - 2nd Int. Conf. Intell. Netw. Collab. Syst. INCOS 2010, pp. 356–361, 2010.
Pankanti, S., Prabhakar, S. & Jain, A.K. (2002). On the Individuality of Fingerprints, IEEE Transactions on Pattern Analysis and Machine Intelligence 24 (2002) 1010–1025.
Peralta, D., Garc´ıa, S., Benitez, J.M. & Herrera, F. (2017). Minutiae-Based Fingerprint Matching Decomposition: Methodology for Big Data Frameworks, Information Sciences (2017), DOI: 10.1016/j.ins.2017.05.001.
Peralta, D., Galarc, M., Triguerod, I., Paternainc, D., Garcíaa, S., Barrenecheac, E., Beníteza, J.M., Bustincec, H. & Herreraa, F. (2015).Asurvey on fingerprint minutiae- based local matching for verification and identification: Taxonomy and experimental evaluation, Inform. Sci. (2015), http://dx.doi.org/10.1016/j.ins.2015.04.013
Peralt, D., Triguero, I., Sanchez-Reillo, R., Herrera, F & Benitez, J.M. (2013). Fast fingerprint identification for large databases, Pattern Recognition (2013), http://dx.doi. org/10.1016/j.patcog.2013.08.002i.
Peralta, D., Triguero, I., Sanchez-Reillo, R., Herrera, F., Ben´ıtez, J.M. (2014). Fast fingerprint identification for large databases. Pattern Recogn. 47(2), 588–602 (2014).
Phillips, J. (2007). High Performance Computing with CUDA – Case Study: Molecular Dynamics; Super Computing Workshop.
Pillarichie, R. & Suyanto. (2012). Algoritma Genetika Dengan Local Search Untuk Penjadwalan Kuliah, Skripsi S1, Fakultas Teknik Informatika, Telkom University.
Poonam, G. (2009). Comparison between Memetic algorithm and Genetic algorithm for the cryptanalysis of Simplified Data Encryption Standard algorithm, International Journal of Network Security & Its Applications (IJNSA), Vol.1, No 1.
Potti S., Pothiraj S. (2011) GPGPU Implementation of Parallel Memetic Algorithm for VLSI Floorplanning Problem. In: Nagamalai D., Renault E., Dhanuskodi
M. (eds) Trends in Computer Science, Engineering and Information Technology.
CCSEIT 2011. Communications in Computer and Information Science, vol 204, pp. 432-441. Springer, Berlin, Heidelberg. DOI: DOIhttps://doi.org/10.1007/978-3-642-24043- 0_44
Radtke P.V.W., Wong T., Sabourin R. (2005) A Multi-objective Memetic Algorithm for Intelligent Feature Extraction. In: Coello Coello C.A., Hernández Aguirre A., Zitzler E. (eds) Evolutionary Multi-Criterion Optimization. EMO 2005. Lecture Notes in Computer Science, vol 3410, pp. 767-781. Springer, Berlin, Heidelberg. DOI: https://doi.org/10.1007/978-3-540-31880-4_53
Ridao, M., Riquelme, J., Camacho, E. & Toro, M. (1998). An evolutionary and local search algorithm for planning two manipulators motion. Lecture Notes in Computer Science. Lecture Notes in Computer Science. Springer-Verlag. 1416: 105–114. DOI:10.1007/3-540-64574-8_396. ISBN 3-540-64574-8.
Roy, T.K. & Gerber, R.B. (2013). Vibrational self-consistent field calculations for spectroscopy of biological molecules: new algorithmic developments and applications, Phys. Chem. Chem. Phys.: PCCP 15 (24) (2013) 9468–9492.
Ruiz, L.G.B., Capel, M.I. & Pegalajar, M.C. (2018). Parallel memetic algorithm for training recurrent neural networks for the energy efficiency problem, Applied Soft Computing Journal (2018), https://doi.org/10.1016/j.asoc.2018.12.028
Sahid. (1997) A Study on University Timetabling. Thesis, Department of Mathematics, The University of Queensland Australia.
Seidenberg, M. S. & McClelland, J. L. (1989) A distributed, developmental model of word recognition and naming. Psychological review 96 (1989) 523–568.
Senate Energy and Natural Resources Committee (SENRC). Next Frontier in High-Performance Computing to Usher in New Chapter in Scientific Discovery. news release, August 1, 2012, https://www.highbeam.com/doc/1P3-2725052741.html
Shao, Y. et.al. (2006). Advances in methods and algorithms in a modern quantum chemistry program package, Physical Chemistry Chemical Physics: PCCP, 8(27), July 2006, pp. 3172–3191.
Sheng, W., Howells, G., Fairhurst, M. & Deravi, F. (2007). A memetic fingerprint matching algorithm, IEEE Trans. Inf. Forensics Secur., vol. 2, no. 3, pp. 402–411, 2007.
Shi, W., Wahba, G., Irizarry, R.A., Bravo, H.C. & Wright, S.J. (2012). The partitioned LASSO-patternsearch algorithm with application to gene expression data, BMC Bioinformatics 13 (1) (2012).
Shimpi, A.L. (2012). Inside the Titan Supercomputer: 299K AMD x86 Cores and 18.6K NVIDIA GPUs. Anand Tech, October31,2012,http://www.anandtech.com/show/6421/inside-ttitansupercomputer-299k-amd-x86-cores-and-186k-nvidia-gpu-cores
Smith J. E. (2007). Coevolving Memetic Algorithms: A Review and Progress Report. IEEE Transactions on Systems Man and Cybernetics - Part B. 37 (1): 6–17. doi:10.1109/TSMCB.2006.883273.
Stamatakis, A. & Ott, M. (2008) Exploiting fine-grained parallelism in the phylogenetic likelihood function with mpi, pthreads, and openmp: a performance study. in: Proceedings of the 3rd International Conference on Pattern Recognition in Bioinformatics, Springer-Verlag, 2008, pp. 424–435.
Stone, H. (1992) High-Performance Computer Architecture. Addison-Wesley Longman Publishing Co., Inc.
Tan KC, Lee TH & Khor EF. (2001). Evolutionary algorithms with dynamic population size and local exploration for multiobjective optimization. IEEE Transactions on Evolutionary Computation, 5(6): 565-588. DOI: 10.1109/4235.974840
Techopedia. (2016). High-Performance Computing (HPC). (diakses 10 Januari 2020) https://www.techopedia.com/definition/4595/high-performance-computing-hpc.
The National Institute for Computational Sciences (NICS). What is HPC?. (diakses 10 Januari 2020),
Tirronen, V., Neri, F., Kärkkäinen, T., Majava, K. & Rossi, T. (2008). An Enhanced Memetic Differential Evolution in Filter Design for Defect Detection in PaperProduction. Journal Evolutionary. Computation, vol. 16, no. 4, pp. 529–555, Springer- Verlag Berlin, 2008. DOI: 10.1162/evco.2008.16.4.529
Ulder, N.L.J., Aarts, E.H.L., Bandelt, H.J., Laarhoven, P.J.M., Pesch, E. (1991). Genetic local search algorithms for the traveling salesman problem, in: H.P. Schwefel, R. Manner (Eds.), Parallel Problem Solving from Nature––Proceedings of 1st Workshop, PPSN I, Lecture Notes in Computer Science, vol. 496, Springer, Berlin, Germany, 1991, pp. 109–116.
Urselmann, M., Barkmann, S., Sand, G. & Engell, S. (2011). A memetic algorithm for global optimization in chemical process synthesis problems, IEEE Trans. Evol. Comput. 15 (5) (2011). 659–283.
Varshney, K.R., Willsky, A.S. (2011). Linear dimensionality reduction for margin-based classification: High-dimensional data and sensor networks, IEEE Trans. Signal Process. 59 (6) (2011) 2496–2512.
Wan, Y., Zhong, Y. & Ma, A. (2019). Fully Automatic Spectral-Spatial Fuzzy Clustering Using an Adaptive Multiobjective Memetic Algorithm for Multispectral Imagery. IEEE Transactions on Geoscience and Remote Sensing, vol. 57, No. 4. Pp. 2324-2340. DOI: 10.1109/TGRS.2018.2872875
Wang, Y., Wang, L., Cheung, Y.M. & Yuen, P.C. (2015). Learning compact binary codes for hash-based fingerprint indexing, IEEE Transactions on Information Forensics and Security 10 (2015) 1603–1616.
Wehrens, R., Lucasius, C., Buydens, L. & Kateman, G. (1993). HIPS, A hybrid self-adapting expert system for nuclear magnetic resonance spectrum interpretation using genetic algorithms. Analytica Chimica Acta. 277 (2): 313–324. doi:10.1016/0003- 2670(93)80444-P.
Welekar, R. & Thakur, N.V. (2019). An Enhanced Approach to Memetic Algorithm Used for Character Recognition, Third International Congress on Information and Communication Technology, vol. 797. Springer Singapore, 2019.
Yamada, T., & Nakano, R. (1991). Scheduling by genetic local search algorithms with multi-step crossover, in: H.P. Schwefel, R. Manner (Eds.), Proceedings of 4th Conference on Parallel Problem Solving from Nature––Proceedings of 1st Workshop, PPSN I, Lecture Notes in Computer Science, vol. 496, Springer, Berlin, Germany, 1991, pp. 960–969.
Yang, S., Cheng, K., Wang, M., Xie, D. & Jiao, L. (2013). High resolution range- reflectivity estimation of radar targets via compressive sampling and Memetic Algorithm. Inf. Sci. (Ny)., vol. 252, pp. 144–156, 2013. https://dx.doi.org/10.1016/j.ins.2013.06.029
Zhang, Y. & Zhong, Y. (2015). Sub-pixel mapping based on memetic algorithm for hyperspectral imagery, Int. Geosci. Remote Sens. Symp., vol. 2015-Novem, pp. 393– 396, 2015.
Zhang, M., Ma, J., Gong, M., Li, H. & Liu, J. (2017). Memetic algorithm based feature selection for hyperspectral images classification, 2017 IEEE Congr. Evol. Comput. CEC 2017 - Proc., no. 2, pp. 495–502, 2017.
Zhao, Y., Sheong, F.K., Sun, J., Sander, P. & Huang, X. (2013). A fast parallel clustering algorithm for molecular simulation trajectories, J. Comput. Chem. 34 (2) (2013) 95–104.
Zhou, D., Fang, Y., Botzheim, J., Kubota & Liu, H. (2016). Bacterial memetic algorithm based feature selection for surface EMG based hand motion recognition in long-term use. IEEE Symposium Series on Computational Intelligence (SSCI), Athens, 2016, pp. 1-7. doi: 10.1109/SSCI.2016.785024
Zhu, Z., Jia, S. & Ji, Z. (2010). Affinity propagation based memetic band selection on hyperspectral imagery datasets. IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010. DOI: 10.1109/CEC.2010.5586533
Zhu, Z., Ong, Y. S. & Dash, M. (2007). Markov Blanket-Embedded Genetic Algorithm for Gene Selection. Pattern Recognition. 49 (11): 3236–3248. doi:10.1016/j.patcog.2007.02.007.
Zhu, Z., Ong, Y. S. & Dash, M. (2007). Wrapper-Filter Feature Selection Algorithm Using A Memetic Framework. IEEE Transactions on Systems, Man and .
Zhu, Z., Ong, Y.S. & Zurada, M. (2008). Simultaneous Identification of Full Class Relevant and Partial Class Relevant Genes. IEEE/ACM Transactions on Computational Biology and Bioinformatics.
