Recent Advances in Evolutionary Multi-objective Optimization / edited by Slim Bechikh, Rituparna Datta, Abhishek Gupta
(Adaptation, Learning, and Optimization. ISSN:18674542 ; 20)
| データ種別 | 電子ブック |
|---|---|
| 版 | 1st ed. 2017. |
| 出版者 | (Cham : Springer International Publishing : Imprint: Springer) |
| 出版年 | 2017 |
| 大きさ | XII, 179 p. 42 illus., 27 illus. in color : online resource |
書誌詳細を非表示
| 一般注記 | Multi-objective Optimization: Classical and Evolutionary Approaches -- Dynamic Multi-objective Optimization using Evolutionary Algorithms: A Survey -- Evolutionary Bilevel Optimization: An Introduction and Recent Advances -- Many-objective Optimization using Evolutionary Algorithms: A Survey -- On the Emerging Notion of Evolutionary Multitasking: A Computational Analog of Cognitive Multitasking -- Practical Applications in Constrained Evolutionary Multi-objective Optimization This book covers the most recent advances in the field of evolutionary multiobjective optimization. With the aim of drawing the attention of up-andcoming scientists towards exciting prospects at the forefront of computational intelligence, the authors have made an effort to ensure that the ideas conveyed herein are accessible to the widest audience. The book begins with a summary of the basic concepts in multi-objective optimization. This is followed by brief discussions on various algorithms that have been proposed over the years for solving such problems, ranging from classical (mathematical) approaches to sophisticated evolutionary ones that are capable of seamlessly tackling practical challenges such as non-convexity, multi-modality, the presence of multiple constraints, etc. Thereafter, some of the key emerging aspects that are likely to shape future research directions in the field are presented. These include:< optimization in dynamic environments, multi-objective bilevel programming, handling high dimensionality under many objectives, and evolutionary multitasking. In addition to theory and methodology, this book describes several real-world applications from various domains, which will expose the readers to the versatility of evolutionary multi-objective optimization HTTP:URL=https://doi.org/10.1007/978-3-319-42978-6 |
|---|---|
| 著者標目 | Bechikh, Slim editor Datta, Rituparna editor Gupta, Abhishek editor SpringerLink (Online service) |
| 件 名 | LCSH:Computational intelligence LCSH:Artificial intelligence FREE:Computational Intelligence FREE:Artificial Intelligence |
| 分 類 | LCC:Q342 DC23:006.3 |
| 巻冊次 | ebook ; ISBN:9783319429786 
|
| ISBN | 9783319429786 |
| URL | https://doi.org/10.1007/978-3-319-42978-6 |
目次／あらすじ
類似資料
この資料の利用統計
このページへのアクセス回数：2回
※2021年9月12日以降