智能优化方法及其应用

授课老师:连宙辉 副教授

Institute of Computer Science & Technology

Peking University

Phone: 86-10-82529245

Email: lianzhouhui@pku.edu.cn

通知

课程简介

教学要求

课件下载

参考资料



通知:

[2018-09-12] 新学期课程网站正式启用,欢迎同学们选修本课程!

[2018-10-11] 论文讲演顺序出炉,相应论文见下方参考资料,每次课两位同学,第一次做讲演的同学是(陈杰),其他次序如下:(2:潘丽晨,吴俊霖),(3:吕管楠,郭一江),(4:唐树森,叶佐贤),(5:尹航,运乃丹),(6:贺元萍,王牌),(7:闫文添,李帅),(8:孙文奇, 李庆涛),(9:高月,马思源),(10:曾沐焓,刘金昊),(11:杜牧倩,霍强),(12:唐舸宇)

[2018-12-13] 大作业提交截止时间:2019年1月18日10:00pm. 模板在课件区下载.



课程简介:

课程编号:L1701682

课程名称:智能优化方法及其应用

英文名称:Intelligent Optimization Methods and Their Applications

授课对象: 信息学院硕博研究生

周学时/总学时:3/48

学分:3

开课目的:优化计算广泛应用于信息学科的各个研究领域,然而传统优化方法在实际应用中有很大的局限性。为了解决该问题,近年来,各种智能优化算法的研究得到了蓬勃发展,其中有广为人知的遗传算法、模拟退火算法、蚁群算法、神经网络算法等。迄今为止,智能优化算法在各个学科和各种实际应用场合中已经得到了广泛且有效的使用。本课程将紧密跟踪学术界最新发展动态,为信息学科的研究生掌握最新的智能优化技术抛砖引玉,为他们后续开展学术研究打下坚实基础。

教学要求:本课程将系统讲授智能优化方法的基础理论和应用技术,深入探讨学术界最新的研究成果,并结合应用实例进行讲解,使得听课的学生不仅能够全面掌握智能优化方法的核心理论,而且能将其应用到各自相关的研究工作中。成绩评定规则为:平时50%+期末50%


教学要求:

本课程将系统讲授智能优化方法的基础理论和应用技术,深入探讨学术界最新的研究成果,并结合应用实例进行讲解,使得听课的学生不仅能够全面掌握智能优化方法的核心理论,而且能将其应用到各自相关的研究工作中。成绩评定规则为:平时50%+期末50%


课件下载:        Back to top

  1. 大作业:大作业模板

  2. 第零讲课程介绍与内容概述

  3. 第1讲经典优化算法1

  4. 第2讲经典优化算法2

  5. 第3讲伪随机数与蒙特卡洛方法

  6. 第4讲遗传算法1

  7. 第5讲遗传算法2

  8. 第6讲遗传算法3

  9. 第7讲禁忌搜索算法

  10. 第8讲模拟退火算法

  11. 第9讲蚁群算法1

  12. 第10讲蚁群算法2

  13. 第11讲粒子群算法


参考资料:        Back to top

  1. 汪定伟等,智能优化方法,高等教育出版社,2007,ISBN 978-7-04-020886-3

  2. 段海斌等,仿生智能计算,科学出版社,2011

  3. 马昌凤等,最优化计算方法及其MATLAB程序实现,国防工业出版社,2015

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  8. Z. Lian*, A. Godil, P.L. Rosin, X. Sun. A New Convexity Measurement for 3D Meshes, CVPR 2012, pp. 119-126, 2012

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  10. D. Silver, A. Huang, et al. Mastering the game of Go with deep neural networks and tree search, Nature 529, pp. 484-489, 2016

  11. M. Qiu, Z. Ming, et al. Phase-Change Memory Optimization for Green Cloud with Genetic Algorithm, IEEE Transactions on Computers, vol. 64, no. 12, pp. 3528-3540, 2015

  12. D. Sholomon, O. David et al. A Genetic Algorithm-Based Solver for Very Large Jigsaw Puzzles, CVPR 2013

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  15. Z Lun, E Kalogerakis, R Wang, A Sheffer. Functionality preserving shape style transfer, Siggraph Asia 2016

  16. X Fan, et al. Automated view and path planning for scalable multi-object 3D scanning, Siggraph Asia 2016

  17. K Chen, et al. Magic decorator: automatic material suggestion for indoor digital scenes, Siggraph 2015

  18. A. Barman, S. Shah. SHaPE: A Novel Graph Theoretic Algorithm for Making Consensus-based Decisions in Person Re-identification Systems, ICCV 2017

  19. Z. Yu, et al. An adaptive unsupervised approach toward pixel clustering and color image segmentation, Pattern Recognition 2010

  20. C. Sui, et al. Deep feature learning for dummies: A simple auto-encoder training method using Particle Swarm Optimisation, Pattern Recognition Letter 2017

  21. G. Huang, et al. Densely Connected Convolutional Networks, CVPR 2017 (cvpr 2017 best paper)

  22. A. Myronenko, et al. Non-rigid point set registration Coherent Point Drift, NIPS 2006 (陈杰)

  23. L. Xie and A. Yuille Genetic CNN, ICCV 2017 (潘丽晨)

  24. C. Tutum, et al. Functional Generative Design: An Evolutionary Approach to 3D-Printing, GECCO '18 (吴俊霖)

  25. B. Taborda, et al. Shaper-GA: automatic shape generation for modular house design, GECCO '18 (吕管楠)

  26. Y. Lin and T. Yu Investigation of the exponential population scheme for genetic algorithms, GECCO '18 (郭一江)

  27. C. Wang, et al. Evolutionary Generative Adversarial Networks, Arxiv 2018 (唐树森)

  28. D. Yang and J. Deng Shape from Shading through Shape Evolution, CVPR 2018 (叶佐贤)

  29. K. Adamczewski, et al. Discrete Tabu Search for Graph Matching, ICCV 2015 (尹航)

  30. I. Zulj, et al. A hybrid of adaptive large neighborhood search and tabu search for the order-batching problem, European Journal of Operational Research 2018 (运乃丹)

  31. A. Barbu, et al. Feature Selection with Annealing for Computer Vison and Big Data Learning, PAMI 2017 (贺元萍)

  32. Z. Fan, et al. Multibody Motion Segmentation Based on Simulated Annealing, CVPR 2004 (王牌)

  33. K. Khanna and N. Rajpal. Reconstruction of curves from point clouds using fuzzy logic and ant colony optimization, Nerocomputing 2015 (闫文添)

  34. M. Mavrovouniotis, et al. Ant Colony Optimization With Local Search for Dynamic Traveling Salesman Problems, IEEE TRANSACTIONS ON CYBERNETICS 2017 (李帅)

  35. X. Liu, et al. An Energy Efficient Ant Colony System for Virtual Machine Placement in Cloud Computing, IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2018 (孙文奇)

  36. H. Ismkhan. Effective heuristics for ant colony optimization to handle large-scale problems, Swarm and Evolutionary Computation 2017 (李庆涛)

  37. P.R. Lorenzo, et al. Particle swarm optimization for hyper-parameter selection in deep neural networks, GECCO '17 (高月)

  38. M.S. Nobile, et al. Fuzzy Self-Tuning PSO: A settings-free algorithm for global optimization, Swarm and Evolutionary Computation 2018 (马思源)

  39. H. Li, et al. Optimization Algorithm Inspired Deep Neural Network Structure Design, ACML 2018 (曾沐焓)

  40. Y. SAHILLIO?LU. A Genetic Isometric Shape Correspondence Algorithm with Adaptive Sampling, TOG 2018 (刘金昊)

  41. J. Dai, et al. Deformable Convolutional Networks, ICCV 2017 (杜牧倩)

  42. L. Gao. Automatic Unpaired Shape Deformation Transfer, Siggraph Asia 2018 (霍强)

  43. 重要会议 GECCO

  44. 重要杂志 Evolutionary Computation,Swarm and Evolutionary Computation


Last update on Sep. 06, 2017                                         

visits since May. 2015