Abstract
Graphic processing units (GPUs) emerged recently as an exciting new hardware environment for a truly parallel implementation and execution of Nature and Bio-inspired Algorithms with excellent price-to-power ratio. In contrast to common multicore CPUs that contain up to tens of independent cores, the GPUs represent a massively parallel single-instruction multiple-data devices that can nowadays reach peak performance of hundreds and thousands of giga floating-point operations per second. Nature and Bio-inspired Algorithms implement parallel optimization strategies in which a single candidate solution, a group of candidate solutions (population), or multiple populations seek for optimal solution or set of solutions of given problem. Genetic algorithms (GA) constitute a family of traditional and very well-known nature-inspired populational meta-heuristic algorithms that have proved its usefulness on a plethora of tasks through the years. Differential evolution (DE) is another efficient populational meta-heuristic algorithm for real-parameter optimization. Particle swarm optimization (PSO) can be seen as nature-inspired multiagent method in which the interaction of simple independent agents yields intelligent collective behavior. Simulated annealing (SA) is global optimization algorithm which combines statistical mechanics and combinatorial optimization with inspiration in metallurgy. This survey provides a brief overview of the latest state-of-the-art research on the design, implementation, and applications of parallel GA, DE, PSO, and SA-based methods on the GPUs.
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Alba, E., Luque, G., Nesmachnow, S.: Parallel metaheuristics: recent advances and new trends. Int. Trans. Oper. Res. 20(1), 1–48 (2013). doi:10.1111/j.1475-3995.2012.00862.x
Alba, E., Troya, J.M.: A survey of parallel distributed genetic algorithms. Complexity 4(4), 31–52 (1999)
Arabas, J., Maitre, O., Collet, P.: PARADE: a massively parallel differential evolution template for EASEA. In: Proceedings of the 2012 International Conference on Swarm and Evolutionary Computation, SIDE’12, pp. 12–20. Springer, Berlin (2012). doi:10.1007/978-3-642-29353-5_2
Arenas, M.G., Romero, G., Mora, A.M., Castillo, P.A., Merelo, J.J.: GPU parallel computation in bioinspired algorithms: a review. In: Advances in Intelligent Modelling and Simulation, Studies in Computational Intelligence, vol. 422, pp. 113–134. Springer (2012)
Bacardit, J., Llora, X.: Large-scale data mining using genetics-based machine learning. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 3(1), 37–61 (2013)
Bajrami, E., Asic, M., Cogo, E., Trnka, D., Nosovic, N.: Performance comparison of simulated annealing algorithm execution on GPU and CPU. In: MIPRO, 2012 Proceedings of the 35th International Convention, pp. 1785–1788 (2012)
Banzhaf, W., Nordin, P., Keller, R.E., Francone, F.D.: Genetic Programming—An Introduction on the Automatic Evolution of Computer Programs and Its Applications. Morgan Kaufmann, San Francisco, CA (1998)
Banerjee, P., Jones, M., Sargent, J.: Parallel simulated annealing algorithms for cell placement on hypercube multiprocessors. Parallel Distrib. Syst. IEEE Trans. 1(1), 91–106 (1990). doi:10.1109/71.80128
Bertsimas, D., Tsitsiklis, J.: Simulated annealing. Stat. Sci. 8(1), 10–15 (1993)
Bessis, N., Sotiriadis, S., Cristea, V., Pop, F.: Modelling requirements for enabling meta-scheduling in inter-clouds and inter-enterprises. In: Intelligent Networking and Collaborative Systems (INCoS), 2011 Third International Conference on, pp. 149–156 (2011). doi:10.1109/INCoS.2011.120
Bessis, N., Sotiriadis, S., Xhafa, F., Pop, F., Cristea, V.: Meta-scheduling issues in interoperable hpcs, grids and clouds. Int. J. Web Grid Serv. 8(2), 153–172 (2012). doi:10.1504/IJWGS.2012.048403
Buchty, R., Heuveline, V., Karl, W., Weiss, J.P.: A survey on hardware-aware and heterogeneous computing on multicore processors and accelerators. Concurr. Comput. Pract. Exp. 24(7), 663–675 (2012). doi:10.1002/cpe.1904
Cagnoni, S., Bacchini, A., Mussi, L.: OpenCL implementation of particle swarm optimization: a comparison between multi-core CPU and GPU performances. In: Chio, C., Agapitos, A., Cagnoni, S., Cotta, C., Vega, F., Caro, G., Drechsler, R., Ekárt, A., Esparcia-Alcázar, A., Farooq, M., Langdon, W., Merelo-Guervós, J., Preuss, M., Richter, H., Silva, S., Simões, A., Squillero, G., Tarantino, E., Tettamanzi, A., Togelius, J., Urquhart, N., Uyar, A., Yannakakis, G. (eds.) Applications of Evolutionary Computation, Lecture Notes in Computer Science, vol. 7248, pp. 406–415. Springer, Berlin (2012). doi:10.1007/978-3-642-29178-4_41.
Cano, A., Zafra, A., Ventura, S.: Speeding up the evaluation phase of GP classification algorithms on GPUs. Soft Comput. 16(2), 187–202 (2012)
Cantú-Paz, E.: Efficient and Accurate Parallel Genetic Algorithms. Kluwer, Dordrecht (2000)
Cavuoti, S., Garofalo, M., Brescia, M., Pescap, A., Longo, G., Ventre, G.: Genetic algorithm modeling with GPU parallel computing technology. In: Neural Nets and Surroundings, Smart Innovation, Systems and Technologies, vol. 19, pp. 29–39. Springer (2013)
Černý, V.: Thermodynamical approach to the traveling salesman problem: an efficient simulation algorithm. J. Optim. Theory Appl. 45, 41–51 (1985). doi:10.1007/BF00940812
Chen, H., Flann, N., Watson, D.: Parallel genetic simulated annealing: a massively parallel SIMD algorithm. Parallel Distrib. Syst. IEEE Trans. 9(2), 126–136 (1998). doi:10.1109/71.663870
Chen, R.B., Hsieh, D.N., Hung, Y., Wang, W.: Optimizing latin hypercube designs by particle swarm. Stat. Comput., 1–14 (2012). doi:10.1007/s11222-012-9363-3
Cheang, S.M., Leung, K.S., Lee, K.H.: Genetic parallel programming: design and implementation. Evolut. Comput. 14(2), 129–156 (2006)
Chitty, D. M., Malvern, Q.: A data parallel approach to genetic programming using programmable graphics hardware. In: GECCO G07: Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation, pp. 1566–1573, ACM Press (2007)
Chitty, D.: Fast parallel genetic programming: Multi-core CPU versus many-core GPU. Soft Comput. 16(10), 1795–1814 (2012)
Choong, A., Beidas, R., Zhu, J.: Parallelizing simulated annealing-based placement using gpgpu. In: Field Programmable Logic and Applications (FPL), 2010 International Conference on, pp. 31–34 (2010). doi:10.1109/FPL.2010.17
Clerc, M.: Particle Swarm Optimization. ISTE. Wiley (2010). http://books.google.cz/books?id=Slee72idZ8EC
Czarn, A., MacNish, C., Vijayan, K., Turlach, B.A.: Statistical exploratory analysis of genetic algorithms: the influence of gray codes upon the difficulty of a problem. In: Webb, G.I., Yu, X. (ed.) Australian Conference on Artificial Intelligence, Lecture Notes in Computer Science, vol. 3339, pp. 1246–1252. Springer (2004)
Datta, D., Mehta, S., Shalivahan, Srivastava, R.: Recent Advances in Information Technology (RAIT), 2012 1st International Conference on CUDA based Particle Swarm Optimization for geophysical inversion, pp. 416–420 (2012). doi:10.1109/RAIT.2012.6194456
de Veronese, L., Krohling, R.: Differential evolution algorithm on the GPU with C-CUDA. In: Evolutionary Computation (CEC), 2010 IEEE Congress on, pp. 1–7 (2010). doi:10.1109/CEC.2010.5586219
Desell, T.J., Anderson, D.P., Magdon-Ismail, M., Newberg, H.J., Szymanski, B.K., Varela, C.A.: An analysis of massively distributed evolutionary algorithms. In: IEEE Congress on Evolutionary Computation, pp. 1–8. IEEE (2010)
Engelbrecht, A.: Computational Intelligence: An Introduction, 2nd edn. Wiley, New York, NY (2007)
Fabris, F., Krohling, R.A.: A co-evolutionary differential evolution algorithm for solving min-max optimization problems implemented on GPU using C-CUDA. Expert Syst. Appl. 39(12), 10,324–10,333 (2012). doi:10.1016/j.eswa.2011.10.015, http://www.sciencedirect.com/science/article/pii/S0957417411015004
Ferreiro, A., García, J., López-Salas, J., Vázquez, C.: An efficient implementation of parallel simulated annealing algorithm in GPUs. J. Glob. Optim., 1–28 (2012). doi:10.1007/s10898-012-9979-z
Franco, M.A., Krasnogor, N., Bacardit, J.: Speeding up the evaluation of evolutionary learning systems using GPGPUs. In: Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation, GECCO10, pp. 1039–1046. ACM, New York, NY (2010)
Frishman, Y., Tal, A.: Multi-level graph layout on the GPU. IEEE Trans. Vis. Comput. Graphics 13(6), 1310–1319 (2007). doi:10.1109/TVCG.2007.70580
Gallego, R., Alves, A., Monticelli, A., Romero, R.: Parallel simulated annealing applied to long term transmission network expansion planning. Power Syst. IEEE Trans. 12(1), 181–188 (1997). doi:10.1109/59.574938
General-purpose computation on graphics hardware. http://www.gpgpu.org. Accessed Jul 2013
Hager, G., Zeiser, T., Wellein, G.: Data access optimizations for highly threaded multi-core cpus with multiple memory controllers. In: Parallel and Distributed Processing. IPDPS 2008. IEEE International Symposium on, pp. 1–7 (2008). doi:10.1109/IPDPS.2008.4536341
Han, Y., Roy, S., Chakraborty, K.: Optimizing simulated annealing on gpu: a case study with ic floorplanning. In: Quality Electronic Design (ISQED), 2011 12th International Symposium on, pp. 1–7 (2011). doi:10.1109/ISQED.2011.5770735
Harding, S., Banzhaf, W.: Fast genetic programming on GPUs. Genet. Program. 4445(3), 90–101 (2007)
Harding, S.: Genetic Programming on Graphics Processing Units Bibliography. http://www.gpgpgpu.com. Accessed Jul 2013
Hofmann, J., Limmer, S., Fey, D.: Performance investigations of genetic algorithms on graphics cards. Swarm Evolut. Comput. 12, 33–47 (2013)
Hung, Y., Wang, W.: Accelerating parallel particle swarm optimization via GPU. Optim. Methods Softw. 27(1), 33–51 (2012)
Hwu, W.W.: Illinois ECE 498AL: programming massively parallel processors. In: Lecture 13: Reductions and Their Implementation. http://nanohub.org/resources/7376 (2009)
Jaroš, J.: Jiri Jaros’s software website, http://www.fit.vutbr.cz/jarosjir/prods.php.en Accessed Jul 2013
Jaroš, J., Pospíchal, P.: A fair comparison of modern CPUs and GPUs running the genetic algorithm under the knapsack benchmark. In: Di Chio, C. et al. (eds.) Applications of Evolutionary Computation. Lecture Notes in Computer Science, pp. 426–435. Springer, Heidelberg (2012). doi:10.1007/978-3-642-29178-4_43
Jaroš, J.: Multi-GPU island-based genetic algorithm for solving the knapsack problem. In: IEEE Congress on Evolutionary Computation, pp. 1–8 (2012)
Jayaraman, R., Darema, F.: Error tolerance in parallel simulated annealing techniques. In: Computer Design: VLSI in Computers and Processors. ICCD ’88., Proceedings of the 1988 IEEE International Conference on, pp. 545–548 (1988). doi:10.1109/ICCD.1988.25759
Juillé, H., Pollack, J.B.: Massively parallel genetic programming. In: Advances in Genetic Programming vol. 2, chapter 17, pp. 339–358. MIT Press (1996)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Neural Networks, Proceedings., IEEE International Conference on, vol. 4, pp. 1942–1948 (1995). doi:10.1109/ICNN.1995.488968
Kilic, O., El-Araby, E., Nguyen, Q., Dang, V.: Bio-inspired optimization for electromagnetic structure design using full-wave techniques on GPUs. In: International Journal of Numerical Modelling: Electronic Networks, Devices and Fields, pp. n/a–n/a (2013). doi:10.1002/jnm.1878
Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by aimulated annealing. Science 220(4598), 671–680 (1983). doi:10.1126/science.220.4598.671
Krömer, P., Platoš, J., Snášel, V.: Differential evolution for the linear ordering problem implemented on CUDA. In: Smith, A.E. (ed.) Proceedings of the 2011 IEEE Congress on Evolutionary Computation, pp. 790–796. IEEE Computational Intelligence Society, IEEE Press, New Orleans, USA (2011)
Krömer, P., Platoš, J., Snášel, V.: A brief survey of differential evolution on graphic processing Units. In: IEEE Symposium on Differential Evolution (SDE), pp. 157–164 (2013)
Krömer, P., Snášel, V., Platoš, J., Abraham, A.: Many-threaded implementation of Differential Evolution for the CUDA platform. In: Krasnogor, N., Lanzi, P.L. (ed.) GECCO, pp. 1595–1602. ACM (2011)
Langdon, W.B., Harrison, A.P.: GP on SPMD parallel graphics hardware for mega bioinformatics data mining. Soft Comput. 12(12), 1169–1183 (2008)
Langdon, W.B.: Large scale bioinformatics data mining with parallel genetic programming on graphics processing units. In: Cantu-Paz, E., de Vega, F. (ed.). Parallel and Distributed Computational Intelligence. Studies in Computational Intelligence, pp. 113–141. Springer, Berlin (2010)
Langdon, W.B.: Graphics processing units and genetic programming: an overview. Soft Comput. 15, 1657–1669 (2011). doi:10.1007/s00500-011-0695-2
Leskinen, J., Périaux, J.: Distributed evolutionary optimization using Nash games and GPUs–applications to CFD design problems. Comput. Fluids (0) (2012). doi:10.1016/j.compfluid.2012.03.017, http://www.sciencedirect.com/science/article/pii/S0045793012001132
Li, H., Liu, C.: Prediction of protein structures using GPU based simulated annealing. In: Machine Learning and Applications (ICMLA), 2012 11th International Conference on, vol. 1, pp. 630–633 (2012). doi:10.1109/ICMLA.2012.117
Luong, T., Melab, N., Talbi, E.-G.: GPU-based island model for evolutionary algorithms. In: GECCO’10: Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation, pp. 1089–1096. ACM, New York (2010)
Mahfoud, S.W., Goldberg, D.E.: Parallel recombinative simulated annealing: a genetic algorithm. Parallel Comput. 21(1), 1–28 (1995). doi:10.1016/0167-8191(94)00071-H
Maitre, O., Baumes, L.A., Lachiche, N., Corma, A., Collet, P.: Coarse grain parallelization of evolutionary algorithms on GPGPU cards with EASEA. In: Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation, GECCO’09, pp. 1403–1410. ACM, New York, NY (2009)
Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge, MA (1996)
Munawar, A., Wahib, M., Munetomo, M., Akama, K.: Hybrid of genetic algorithm and local search to solve MAX-SAT problem using nVidia CUDA framework. Genet. Programm. Evolvable Mach. 10, 391–415 (2009)
Nashed, Y.S., Ugolotti, R., Mesejo, P., Cagnoni, S.: libCudaOptimize: an open source library of GPU-based metaheuristics. In: Proceedings of the Fourteenth International Conference on Genetic and Evolutionary Computation Conference Companion, GECCO Companion ’12, pp. 117–124. ACM, New York, NY (2012). doi:10.1145/2330784.2330803.
Nashed, Y.S.G., Mesejo, P., Ugolotti, R., Dubois-Lacoste, J., Cagnoni, S.: A comparative study of three GPU-based metaheuristics. In: Proceedings of the 12th International Conference on Parallel Problem Solving from Nature—Volume Part II, PPSN’12, pp. 398–407. Springer, Berlin (2012). doi:10.1007/978-3-642-32964-7_40
Nobile, M., Besozzi, D., Cazzaniga, P., Mauri, G., Pescini, D.: A gpu-based multi-swarm pso method for parameter estimation in stochastic biological systems exploiting discrete-time target series. In: Giacobini, M., Vanneschi, L., Bush, W. (eds.) Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, Lecture Notes in Computer Science, vol. 7246, pp. 74–85. Springer, Berlin (2012). doi:10.1007/978-3-642-29066-4_7.
Nobile, M.S., Besozzi, D., Cazzaniga, P., Mauri, G., Pescini, D.: Estimating reaction constants in stochastic biological systems with a multi-swarm PSO running o GPUs. In: Proceedings of the Fourteenth International Conference on Genetic and Evolutionary Computation Conference Companion, GECCO Companion ’12, pp. 1421–1422. ACM, New York, NY (2012). doi:10.1145/2330784.2330964
NVIDIA: NVIDIA CUDA Programming Guide Accessed Jul 2013
Platoš, J., Snášel, V., Ježowicz, T., Krömer, P., Abraham, A.: A PSO-based document classification algorithm accelerated by the CUDA platform. In: Systems, Man, and Cybernetics (SMC), 2012 IEEE International Conference on, pp. 1936–1941 (2012). doi:10.1109/ICSMC.2012.6378021
Pop, F.: Optimization of resource control for transitions in complex systems. Math. Probl. Eng. 12 (2012). doi:10.1155/2012/625861
Pospíchal, P., Jaroš, J. Schwarz, J.: Parallel genetic algorithm on the CUDA architecture. In: Di Chio, C. et al. (eds.) Applications of Evolutionary Computation. Lecture Notes in Computer Science, pp. 442–451. Springer, Heidelberg (2010). doi:10.1007/978-3-642-12239-2_46
Price, K.V., Storn, R.M., Lampinen, J.A.: Differential Evolution a Practical Approach to Global Optimization. Natural Computing Series. Springer, Berlin (2005) http://www.springer.com/west/home/computer/foundations?SGWID=4-156-22-32104365-0&teaserId=68063&CENTER_ID=69103
Pryor, G., Lucey, B., Maddipatla, S., McClanahan, C., Melonakos, J., Venugopalakrishnan, V., Patel, K., Yalamanchili, P., Malcolm, J.: High-level GPU computing with Jacket for Matlab and C/C++. In: Modeling and Simulation for Defense Systems and Applications VI, vol. 8060, pp. 806,005–806,005–6 (2011). doi:10.1117/12.884899
Qin, A.K., Raimondo, F., Forbes, F., Ong, Y.S.: An improved CUDA-based implementation of differential evolution on GPU. In: Proceedings of the Fourteenth International Conference on Genetic and Evolutionary Computation Conference, GECCO ’12, pp. 991–998. ACM, New York, NY (2012). doi:10.1145/2330163.2330301
Rabinovich, M., Kainga, P., Johnson, D., Shafer, B., Lee, J., Eberhart, R.: Particle Swarm Optimization on a GPU. In: Electro/Information Technology (EIT), 2012 IEEE International Conference on, pp. 1–6 (2012). doi:10.1109/EIT.2012.6220761
Ramirez-Chavez, L.E., Coello Coello, C.A., Rodriguez-Tello, E.: A GPU-based implementation of differential evolution for solving the gene regulatory network model inference problem. In: Proceedings of the Fourth International Workshop on Parallel Architectures and Bioinspired Algorithms, WPABA 2011, pp. 21–30. Galveston Island, TX, USA (2011)
Reguera-Salgado, J., Martin-Herrero, J.: High performance GCP-based Particle Swarm Optimization of orthorectification of airborne pushbroom imagery. In: Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE, International, pp. 4086–4089 (2012). doi:10.1109/IGARSS.2012.6350729
Roberge, V., Tarbouchi, M.: Efficient parallel particle swarm optimizers on GPU for real-time harmonic minimization in multilevel inverters. In: IECON 2012—38th Annual Conference on IEEE Industrial Electronics Society, pp. 2275–2282 (2012). doi:10.1109/IECON.2012.6388882
Roberge, V., Tarbouchi, M.: Parallel particle swarm optimization on graphical processing unit for pose estimation. WSEAS Trans. Comput. 11, 170–179 (2012)
Robilliard, D., Marion-Poty, V., Fonlupt, C.: Genetic programming on graphics processing units. Genet. Program Evolvable Mach., 10, 447–471, Kluwer Academic Publishers (2009)
Rutenbar, R.: Simulated annealing algorithms: an overview. Circuits Devices Mag. IEEE 5(1), 19–26 (1989). doi:10.1109/101.17235
Schröck, M., Vogt, H.: Gauge fixing using overrelaxation and simulated annealing on GPUs. PoS LATTICE2012, 187 (2012)
Sharma, B., Thulasiram, R., Thulasiraman, P.: Portfolio management using particle swarm optimization on GPU. In: Parallel and Distributed Processing with Applications (ISPA), 2012 IEEE 10th International Symposium on, pp. 103–110 (2012). doi:10.1109/ISPA.2012.22
Sharma, B., Thulasiram, R., Thulasiraman, P.: Normalized particle swarm optimization for complex chooser option pricing on graphics processing unit. J. Supercomput., 1–23 (2013). doi:10.1007/s11227-013-0893-z
Simonsen, M., Pedersen, C.N., Christensen, M.H., Thomsen, R.: GPU-accelerated high-accuracy molecular docking using guided differential evolution: real world applications. In: Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, GECCO’11, pp. 1803–1810. ACM, New York, NY (2011). doi:10.1145/2001576.2001818
Souza, D.L., Teixeira, O.N., Monteiro, D.C., Oliveira, R.C.L.A.: A new cooperative evolutionary multi-swarm optimizer algorithm based on CUDA architecture applied to engineering optimization. In: Hatzilygeroudis, I., Palade, V. (ed.) Combinations of Intelligent Methods and Applications, Smart Innovation, Systems and Technologies, vol. 23, pp. 95–115. Springer, Berlin (2013). doi:10.1007/978-3-642-36651-2_6
Stivala, A., Stuckey, P., Wirth, A.: Fast and accurate protein substructure searching with simulated annealing and GPUs. BMC Bioinform. 11(1), 1–17 (2010). doi:10.1186/1471-2105-11-446
Storn, R.: Differential evolution design of an IIR-filter. In: Proceeding of the IEEE Conference on Evolutionary Computation ICEC, pp. 268–273. IEEE Press (1996)
Storn, R., Price, K.: Differential Evolution—A Simple and Efficient Adaptive Scheme for Global Optimization over Continuous Spaces. Technical Report (1995). http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1.9696
Suganthan, P.N., Hansen, N., Liang, J.J., Deb, K., Chen, Y.P., Auger, A., Tiwari, S.: Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real Parameter Optimization. Technical Report. Nanyang Technological University (2005)
Tagawa, K.: Concurrent differential evolution based on generational model for multi-core CPUs. In: Bui, L.T., Ong, Y.S., Hoai, N.X., Ishibuchi, H., Suganthan, P.N. (eds.) SEAL, Lecture Notes in Computer Science, vol. 7673, pp. 12–21. Springer (2012)
Tang, K., Yao, X., Suganthan, P.N., MacNish, C., Chen, Y.P., Chen, C.M., Yang, Z.: Benchmark Functions for the CEC 2008 Special Session and Competition on Large Scale Global Pptimization. Technical Report, Nature Inspired Computation and Applications Laboratory, USTC (2007). http://nical.ustc.edu.cn/cec08ss.php
Tanese, R.: Distributed genetic algorithms. In: Proceedings of the 3rd International Conference on Genetic Algorithms, pp. 434–439. Morgan Kaufmann Publishers, Burlington, MA (1989)
Tasoulis, D., Pavlidis, N., Plagianakos, V., Vrahatis, M.: Parallel differential evolution. In: Evolutionary Computation, 2004. CEC2004. Congress on, vol. 2, pp. 2023–2029. IEEE (2004)
Tufts, P.: Parallel case evaluation for Genetic Programming. In: 1993 Lectures in Complex Systems, volume VI of Santa Fe Institute Studies in the Science of Complexity, pp. 591–596. Addison-Wesley, Reading, MA (1995)
Ugolotti, R., Nashed, Y., Cagnoni, S.: Real-Time GPU Based Road Sign Detection and Classification. In: Coello, C., Cutello, V., Deb, K., Forrest, S., Nicosia, G., Pavone, M. (eds.) Parallel problem solving from nature—PPSN XII. In: Lecture Notes in Computer Science, vol. 7491, pp. 153–162. Springer, Berlin (2012). doi:10.1007/978-3-642-32937-1_16
Ugolotti, R., Nashed, Y.S., Mesejo, P., Špela Ivekovič, Mussi, L., Cagnoni, S.: Particle swarm optimization and differential evolution for model-based object detection. Appl. Soft Comput. (0), (2012). doi:10.1016/j.asoc.2012.11.027, http://www.sciencedirect.com/science/article/pii/S156849461200511X
Valdez, F., Melin, P., Castillo, O.: Bio-inspired optimization methods on graphic processing unit for minimization of complex mathematical functions. In: Castillo, O., Melin, P., Kacprzyk, J. (eds.) Recent Advances on Hybrid Intelligent Systems, Studies in Computational Intelligence, vol. 451, pp. 313–322. Springer, Berlin (2013). doi:10.1007/978-3-642-33021-6_25.
Wachowiak, M.P., Foster, A.E.L.: GPU-based asynchronous global optimization with particle swarm. J. Phys. Conf. Ser. 385(1), 012,012 (2012). http://stacks.iop.org/1742-6596/385/i=1/a=012012
Wang, H., Rahnamayan, S., Wu, Z.: Parallel differential evolution with self-adapting control parameters and generalized opposition-based learning for solving high-dimensional optimization problems. J. Parallel Distrib. Comput. 73(1), 62–73 (2013). doi:10.1016/j.jpdc.2012.02.019. http://www.sciencedirect.com/science/article/pii/S0743731512000639. Metaheuristics on GPUs
Wang, L., Yang, B., Chen, Y., Zhao, X.: Predict the hydration of portland cement using differential evolution. In: Evolutionary Computation (CEC), 2012 IEEE Congress on, pp. 1–5 (2012). doi:10.1109/CEC.2012.6252984
Wilt, N.: The CUDA Handbook: A Comprehensive Guide to GPU Programming. Addison-Wesley, Reading, MA (2013)
Wong, M., Wong, T.: Implementation of parallel genetic algorithms on graphics processing units. In: Intelligent and Evolutionary Systems, pp. 197–216. Springer, Berlin (2009)
Wong, T.T., Wong, M.L.: Parallel evolutionary algorithms on consumer-level graphics processing unit. In: Parallel Evolutionary Computations, pp. 133–155 (2006)
Wu, A.S., Lindsay, R.K., Riolo, R.: Empirical observations on the roles of crossover and mutation. In: Bäck, T. (ed.) Proceedings of the Seventh International Conference on Genetic Algorithms, pp. 362–369. Morgan Kaufmann, San Francisco, CA (1997). citeseer.ist.psu.edu/wu97empirical.html.
Xiao, C., Qiming, W.: Modified parallel differential evolution algorithm with local spectral feature to solve data registration problems. In: Computer Science and Network Technology (ICCSNT), 2011 International Conference on, vol. 3, pp. 1386–1389 (2011). doi:10.1109/ICCSNT.2011.6182223
Zhang, Z., Seah, H.S.: CUDA acceleration of 3D dynamic scene reconstruction and 3D motion estimation for motion capture. In: Parallel and Distributed Systems (ICPADS), 2012 IEEE 18th International Conference on, pp. 284–291 (2012). doi:10.1109/ICPADS.2012.47
Zhang, S., He, Z.: Implementation of parallel genetic algorithm based on CUDA. In: Cai, Z., Li, Z., Kang, Z., Liu, Y. (eds.) ISICA 2009. LNCS, vol. 5821, pp. 24–30. Springer, Heidelberg (2009)
Zhao, J., Wang, W., Pedrycz, W., Tian, X.: Online parameter optimization-based prediction for converter gas system by parallel strategies. Control Syst. Technol. IEEE Trans. 20(3), 835–845 (2012). doi:10.1109/TCST.2011.2134098
Zhu, W.: Massively parallel differential evolution—pattern search optimization with graphics hardware acceleration: an investigation on bound constrained optimization problems. J. Glob. Optim., 1–21 (2010). doi:10.1007/s10898-010-9590-0
Zhu, W., Li, Y.: GPU-accelerated differential evolutionary markov chain Monte Carlo method for multi-objective optimization over continuous space. In: Proceeding of the 2nd Workshop on Bio-Inspired Algorithms for Distributed Systems, BADS ’10, pp. 1–8. ACM, New York, NY (2010). doi:10.1145/1809018.1809021
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This work was supported by the European Regional Development Fund in the IT4Innovations Centre of Excellence project (CZ.1.05/1.1.00/02.0070) and by the Bio-Inspired Methods: research, development and knowledge transfer project, Reg. No. CZ.1.07/2.3. 00/20.0073 funded by Operational Programme Education for Competitiveness, co-financed by ESF and state budget of the Czech Republic.
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Krömer, P., Platoš, J. & Snášel, V. Nature-Inspired Meta-Heuristics on Modern GPUs: State of the Art and Brief Survey of Selected Algorithms. Int J Parallel Prog 42, 681–709 (2014). https://doi.org/10.1007/s10766-013-0292-3
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DOI: https://doi.org/10.1007/s10766-013-0292-3