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Titlebook: How to Solve It: Modern Heuristics; Zbigniew Michalewicz,David B. Fogel Textbook 2004Latest edition Springer-Verlag Berlin Heidelberg 2004

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发表于 2025-3-21 16:43:40 | 显示全部楼层 |阅读模式
书目名称How to Solve It: Modern Heuristics
编辑Zbigniew Michalewicz,David B. Fogel
视频video
图书封面Titlebook: How to Solve It: Modern Heuristics;  Zbigniew Michalewicz,David B. Fogel Textbook 2004Latest edition Springer-Verlag Berlin Heidelberg 2004
描述No pleasure lasts long unless there is variety in it. Publilius Syrus, Moral Sayings We‘ve been very fortunate to receive fantastic feedback from our readers during the last four years, since the first edition of How to Solve It: Modern Heuristics was published in 1999. It‘s heartening to know that so many people appreciated the book and, even more importantly, were using the book to help them solve their problems. One professor, who published a review of the book, said that his students had given the best course reviews he‘d seen in 15 years when using our text. There can be hardly any better praise, except to add that one of the book reviews published in a SIAM journal received the best review award as well. We greatly appreciate your kind words and personal comments that you sent, including the few cases where you found some typographical or other errors. Thank you all for this wonderful support.
出版日期Textbook 2004Latest edition
关键词Descriptive statistics; Random variable; Regression; Variable; algorithm; algorithms; evolutionary algorit
版次2
doihttps://doi.org/10.1007/978-3-662-07807-5
isbn_softcover978-3-642-06134-9
isbn_ebook978-3-662-07807-5
copyrightSpringer-Verlag Berlin Heidelberg 2004
The information of publication is updating

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Basic Concepts (1) the representation, (2) the objective, and (3) the evaluation function. The representation encodes alternative candidate solutions for manipulation, the objective describes the purpose to be fulfilled, and the evaluation function returns a specific value that indicates the quality of any partic
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Escaping Local Optimaommon pattern. Either they guarantee discovering the global solution, but are too expensive (i.e., too time consuming) for solving typical real-world problems, or else they have a tendency of “getting stuck” in local optima. Since there is almost no chance to speed up algorithms that guarantee findi
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Designing Evolutionary Algorithms iterations of random variation and selection. Random variation provides the mechanism for discovering new solutions. Selection determines which solutions to maintain as a basis for further exploration. Metaphorically, the search is conducted on a landscape of hills and valleys (see figure 7.1), whi
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The Traveling Salesman Problemminimum-distance solution. This is where the similarities end, however, because there aren’t any tricks for finding perfect solutions to the TSP. The problem is NP-hard [1791: there are no known algorithms for finding perfect solutions that have a complexity that grows as only a polynomial function
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Constraint-Handling Techniquesonstraints. Dhar and Ranganathan [102] wrote:.We would only amend this by removing the word “virtually.” In the most extreme case, if a real-world problem is worth solving, then it is worth solving within your lifetime, or at least the foreseeable life span of the human race. The eventual nova of ou
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