Evolutionary Learning: Advances in Theories and Algorithms
暫譯: 進化學習：理論與演算法的進展

Zhou, Zhi-Hua, Yu, Yang, Qian, Chao

Evolutionary Learning: Advances in Theories and Algorithms
  • 出版商: Springer
  • 出版日期: 2019-06-03
  • 售價: $6,400
  • 貴賓價: 9.5 折 $6,080
  • 語言: 英文
  • 頁數: 361
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 9811359555
  • ISBN-13: 9789811359552
  • 相關分類: Algorithms-data-structures

    • 海外代購書籍(需單獨結帳)

相關主題

商品描述

Many machine learning tasks involve solving complex optimization problems, such as working on non-differentiable, non-continuous, and non-unique objective functions; in some cases it can prove difficult to even define an explicit objective function. Evolutionary learning applies evolutionary algorithms to address optimization problems in machine learning, and has yielded encouraging outcomes in many applications. However, due to the heuristic nature of evolutionary optimization, most outcomes to date have been empirical and lack theoretical support. This shortcoming has kept evolutionary learning from being well received in the machine learning community, which favors solid theoretical approaches.

Recently there have been considerable efforts to address this issue. This book presents a range of those efforts, divided into four parts. Part I briefly introduces readers to evolutionary learning and provides some preliminaries, while Part II presents general theoretical tools for the analysis of running time and approximation performance in evolutionary algorithms. Based on these general tools, Part III presents a number of theoretical findings on major factors in evolutionary optimization, such as recombination, representation, inaccurate fitness evaluation, and population. In closing, Part IV addresses the development of evolutionary learning algorithms with provable theoretical guarantees for several representative tasks, in which evolutionary learning offers excellent performance.

商品描述(中文翻譯)

許多機器學習任務涉及解決複雜的優化問題，例如處理不可微分、非連續和非唯一的目標函數；在某些情況下，甚至很難明確定義一個目標函數。進化學習應用進化算法來解決機器學習中的優化問題，並在許多應用中取得了令人鼓舞的成果。然而，由於進化優化的啟發式特性，迄今為止大多數結果都是經驗性的，缺乏理論支持。這一缺陷使得進化學習在機器學習社群中未能受到廣泛接受，因為該社群更偏好堅實的理論方法。

最近，針對這一問題已經進行了相當多的努力。本書介紹了這些努力的範圍，分為四個部分。第一部分簡要介紹進化學習並提供一些基礎知識，而第二部分則呈現進化算法運行時間和近似性能分析的一般理論工具。基於這些一般工具，第三部分提出了關於進化優化中主要因素的若干理論發現，例如重組、表示、不準確的適應度評估和種群。最後，第四部分探討了具有可證明理論保證的進化學習算法的開發，針對幾個具有代表性的任務，其中進化學習表現出色。

作者簡介

Zhi-Hua Zhou is a Professor, founding director of the LAMDA Group, Head of the Department of Computer Science and Technology of Nanjing University, China. He authored the books "Ensemble Methods: Foundations and Algorithms" (2012) and "Machine Learning" (in Chinese, 2016), and published many papers in top venues in artificial intelligence and machine learning. His H-index is 89 according to Google Scholar. He founded ACML (Asian Conference on Machine Learning), and served as chairs for many prestigious conferences such as AAAI 2019 program chair, ICDM 2016 general chair, etc., and served as action/associate editor for prestigious journals such as PAMI, Machine Learning journal, etc. He is a Fellow of the ACM, AAAI, AAAS, IEEE and IAPR.

Yang Yu is an associate Professor of Nanjing University, China. His research interests are in artificial intelligence, including reinforcement learning, machine learning, and derivative-free optimization. He was recognized in "AI's 10 to Watch" by IEEE Intelligent Systems 2018, and received several awards/honors including the PAKDD Early Career Award, IJCAI'18 Early Career Spotlight talk, National Outstanding Doctoral Dissertation Award, China Computer Federation Outstanding Doctoral Dissertation Award, PAKDD'08 Best Paper Award, GECCO'11 Best Paper (Theory Track), etc. He is a Junior Associate Editor of Frontiers of Computer Science, and an Area Chair of ACML'17, IJCAI'18, and ICPR'18.

Chao Qian is an associate Researcher of University of Science and Technology of China, China. His research interests are in artificial intelligence, evolutionary computation and machine learning. He has published over 20 papers in leading international journals and conference proceedings, including Artificial Intelligence, Evolutionary Computation, IEEE Transactions on Evolutionary Computation, Algorithmica, NIPS, IJCAI, AAAI, etc. He has won the ACM GECCO 2011 Best Paper Award (Theory Track) and the IDEAL 2016 Best Paper Award. He has also been chair of IEEE Computational Intelligence Society (CIS) Task Force "Theoretical Foundations of Bio-inspired Computation".

作者簡介(中文翻譯)

周志華是中國南京大學計算機科學與技術系的教授、LAMDA小組的創始主任。他著有《集成方法：基礎與算法》（2012年）和《機器學習》（中文，2016年）等書籍，並在人工智慧和機器學習的頂尖會議上發表了多篇論文。根據Google Scholar，他的H指數為89。他創立了ACML（亞洲機器學習會議），並擔任過許多知名會議的主席，如AAAI 2019的程序主席、ICDM 2016的總主席等，並擔任過《PAMI》、《Machine Learning journal》等知名期刊的行動/副編輯。他是ACM、AAAI、AAAS、IEEE和IAPR的會士。

余洋是中國南京大學的副教授。他的研究興趣包括人工智慧、強化學習、機器學習和無導數優化。他在2018年被IEEE Intelligent Systems評選為「AI's 10 to Watch」，並獲得多項獎項/榮譽，包括PAKDD早期職業獎、IJCAI'18早期職業聚光燈演講、全國優秀博士論文獎、中國計算機學會優秀博士論文獎、PAKDD'08最佳論文獎、GECCO'11最佳論文（理論組）等。他是《Frontiers of Computer Science》的初級副編輯，並擔任ACML'17、IJCAI'18和ICPR'18的區域主席。

錢超是中國科學技術大學的副研究員。他的研究興趣包括人工智慧、進化計算和機器學習。他在《Artificial Intelligence》、《Evolutionary Computation》、《IEEE Transactions on Evolutionary Computation》、《Algorithmica》、《NIPS》、《IJCAI》、《AAAI》等領先的國際期刊和會議論文集中發表了20多篇論文。他曾獲得ACM GECCO 2011最佳論文獎（理論組）和IDEAL 2016最佳論文獎。他還曾擔任IEEE計算智能學會（CIS）任務小組「生物啟發計算的理論基礎」的主席。

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