计算机科学 ›› 2018, Vol. 45 ›› Issue (11A): 234-237.
楼浩锋1, 张端2
LOU Hao-feng1, ZHANG Duan2
摘要: 为了改进协方差矩阵自适应进化策略(CMA-ES)的性能,提出了一种高斯过程协助下的协方差矩阵自适应进化策略(GPACMA-ES)。该策略利用CMA-ES中的协方差矩阵构建核函数,引入高斯过程,在线学习历史经验,并根据历史经验预测全局最优解的最有前景区域,有效地降低了适应度函数的评价次数。同时,为了提高群体的搜索效率,引入了置信区间。群体在置信区间内更高效地采样,使得算法具备更快的收敛速度和全局寻优能力。最后,将GPACMA-ES算法应用于医学图像配准中,配准精度和效率均高于标准的CMA-ES算法。
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[1]LEGG P A,ROSIN P L,MARSHALL D,et al.Feature Neighbourhood Mutual Information for multi-modal image registration:An application to eye fundus imaging[J].Pattern Recognition,Elsevier,2015,48(6):1937-1946. [2]SONG G,HAN J,ZHAO Y,et al.A Review on Medical Image Registration as an Optimization Problem[J].Current Medical Imaging Reviews,2017,13(3):274-283. [3]PANDA R,AGRAWAL S,SAHOO M,et al.A novel evolutio-nary rigid body docking algorithm for medical image registration[J].Swarm & Evolutionary Computation,2017,33:108-118. [4]WINTER S,BRENDEL B,PECHLIVANIS I,et al.Registration of CT and intraoperative 3-D ultrasound images of the spine using evolutionary and gradient-based methods[J].IEEE Tran-sactions on Evolutionary Computation,2008,12(3):284-296. [5]刘哲,宋余庆,王栋栋.自适应变异差分算法与Powell算法相结合的医学图像配准[J].计算机科学,2017,44(11):297-300. [6]HANSEN N.The CMA Evolution Strategy:A Tutorial[J/OL].http://www2.fiit.stuba.sk/~kvasnicka/Seminar_of_AI/Hansen_CMA_tutorial.pdf. [7]李焕哲,吴志健,汪慎文,等.协方差矩阵自适应演化策略学习机制综述[J].电子学报,2017,45(1):238-245. [8]NISHID K,AKIMOTO Y.Population Size Adaptation for the CMA-ES Based on the Estimation Accuracy of the Natural Gradient[C]∥Genetic and Evolutionary Computation Conference.ACM,2016:237-244. [9]MOHAMMADI H,RICHE R L,TOUBOUL E.Making EGO and CMA-ES Complementary for Global Optimization[M]∥Learning and Intelligent Optimization.Springer International Publishing,2015:287-292. [10]BOUZARKOUNA Z,AUGER A,DING D Y.Investigating the Local-Meta-Model CMAES for Large Population Sizes[M]∥Applications of Evolutionary Computation.Springer Berlin Heidelberg,2010:402-411. [11]LOSHCHILOV I,SCHOENAUER M.Comparison-based optimizers need comparison-based surrogates[C]∥International Conference on Parallel Problem Solving From Nature.Springer-Verlag,2010:364-373. [12]KRUISSELBRINK J W,EMMERICH M T M,DEUTZ A H,et al.A robust optimization approach using Kriging metamodels for robustness approximation in the CMAES[C]∥Evolutionary Computation.IEEE,2010:1-8. [13]别术林.基于互信息的医学图像配准算法研究[D].北京:北京交通大学,2014. [14]HANSEN N,OSTERMEIER A.Adapting arbitrary normal mutation distributions in evolution strategies:the covariance matrix adaptation[C]∥IEEE International Conference on Evolutionary Computation.IEEE,1996:312-317. [15]HOWARD W R.Pattern Recognition and Machine Learning[M]∥Pattern Recognition and Machine Learning.Springer,2006:461-462. [16]SEEGER M.Gaussian processes for machine learning[J].International Journal of Neural Systems,2004,14(2):69-106. [17]LOSHCHILOV I,SCHOENAUER M,SEBAG M.Self-adaptive surrogate-assisted covariance matrix adaptation evolution strategy[C]∥Conference on Genetic and Evolutionary Computation.ACM,2012:321-328. [18]SHAHRIARI B,SWERSKY K,WANG Z,et al.Taking the Human Out of the Loop:A Review of Bayesian Optimization[J].Proceedings of the IEEE,2015,104(1):1-24. [19]SNOEK J,LAROCHELLE H,ADAMS R P.Practical bayesian optimization of machine learning algorithms[C]∥Advances in Neural Information Processing Systems.MIT Press,2012:2951-2959. [20]COLLINS D L,ZIJDENBOS A P,KOLLOKIAN V,et al.Design and construction of a realistic digital brain phantom[J].IEEE Transactions on Medical Imaging,1998,17(3):463-468. |
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