| 書目名稱 | Genetic Algorithms for Machine Learning |
| 編輯 | John J. Grefenstette |
| 視頻video | http://file.papertrans.cn/383/382441/382441.mp4 |
| 圖書封面 |  |
| 描述 | The articles presented here were selected from preliminaryversions presented at the International Conference on GeneticAlgorithms in June 1991, as well as at a special Workshop on GeneticAlgorithms for Machine Learning at the same Conference. .Genetic algorithms are general-purpose search algorithms that useprinciples inspired by natural population genetics to evolve solutionsto problems. The basic idea is to maintain a population of knowledgestructure that represent candidate solutions to the problem ofinterest. The population evolves over time through a process ofcompetition (i.e. survival of the fittest) and controlled variation(i.e. recombination and mutation). ..Genetic Algorithms for Machine Learning. contains articles onthree topics that have not been the focus of many previous articles onGAs, namely concept learning from examples, reinforcement learning forcontrol, and theoretical analysis of GAs. It is hoped that this samplewill serve to broaden the acquaintance of the general machine learningcommunity with the major areas of work on GAs. The articles in thisbook address a number of central issues in applying GAs to machinelearning problems. For example, the choice of appr |
| 出版日期 | Book 1994 |
| 關鍵詞 | algorithms; control; decision model; genetic algorithms; genetics; knowledge; learning; machine learning; mu |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-1-4615-2740-4 |
| isbn_softcover | 978-1-4613-6182-4 |
| isbn_ebook | 978-1-4615-2740-4 |
| copyright | Springer Science+Business Media New York 1994 |
|Archiver|手機版|小黑屋|
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