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深度學習
基于種群概率模型的優化技術-從算法到應用 版權信息
- ISBN：9787313063694
- 條形碼：9787313063694 ; 978-7-313-06369-4
- 裝幀：暫無
- 冊數：暫無
- 重量：暫無
- 所屬分類：>
基于種群概率模型的優化技術-從算法到應用 本書特色
《基于種群概率模型的優化技術:從算法到應用(英文版)》共有9個章節組成，系統地討論了遺傳算法和分布估計算法的基本理論，并在二進制搜尋空間實驗性地比較了幾種分布估算法。在此基礎上深入地論述了構建一類新的分布估計算法的思路和實現方法，*后介紹了分布估計算法在計算機科學、資源管理等領域的一些成功應用實例。《基于種群概率模型的優化技術:從算法到應用(英文版)》可作為從事概率論、數量建模等課程研究的人員參考讀物。
基于種群概率模型的優化技術-從算法到應用 內容簡介
本書較系統地討論了遺傳算法和分布估計算法的基本理論，并在二進制搜尋空間實驗性地比較了幾種分布估算法。在此基礎上深入地論述了構建一類新的分布估計算法的思路和實現方法，*后介紹了分布估計算法在計算機科學、資源管理等領域的一些成功應用實例及分布估計算法的幾種有效改進方法。
基于種群概率模型的優化技術-從算法到應用 目錄
chapter 1 fundamentals and literature
1.1 optimization problems
1.2 canonical genetic algorithm
1.3 individual representations
1.4 mutation
1.5 recombination
1.6 population models
1.7 parent selection
1.8 survivor selection
1.9 summary
chapter 2 the probabilistic model -building genetic algorithms
2.1 introduction
2.2 a simple optimization example
2.3 different eda approaches
2.4 optimization in continuous domains with edas
2.5 summary
chapter 3 an empirical comparison of edas in binary search spaces
3.1 introduction
3.2 experiments
3.3 test functions for the convergence reliability
3.4 experimental results
3.5 summary
chapter 4 development of a new type of edas based on principle of maximum entropy
4.1 introduction
4.2 entropy and schemata
4.3 the idea of the proposed algorithms
4.4 how can the estimated distribution be computed and sampled?
4.5 new algorithms
4.6 empirical results
4.7 summary
chapter 5 applying continuous edas to optimization problems
5.1 introduction
5.2 description of the optimization problems
5.3 edas to test
5.4 experimental description
5.5 summary
chapter 6 optimizing curriculum scheduling problem using eda
6.1 introduction
6.2 optimization problem of curriculum scheduling
6.3 methodology
6.4 experimental results
6.5 summary
chapter 7 recognizing human brain images using edas
7.1 introduction
7.2 graph matching problem
7.3 representing a matching as a permutation
7.4 apply edas to obtain a permutation that symbolizes the solution
7.5 obtaining a permutation with continuous edas
7.6 experimental results
7.7 summary
chapter 8 optimizing dynamic pricing problem with edas and ga
8.1 introduction
8.2 dynamic pricing for resource management
8.3 modeling dynamic pricing
8.4 an ea approaches to dynamic pricing
8.5 experiments and results
8.6 summary
chapter 9 improvement techniques of edas
9.1 introduction
9.2 tradeoffs are exploited by efficiency-improvement techniques
9.3 evaluation relaxation: designing adaptive endogenous surrogates
9.4 time continuation: mutation in edas
9.5 summary
1.1 optimization problems
1.2 canonical genetic algorithm
1.3 individual representations
1.4 mutation
1.5 recombination
1.6 population models
1.7 parent selection
1.8 survivor selection
1.9 summary
chapter 2 the probabilistic model -building genetic algorithms
2.1 introduction
2.2 a simple optimization example
2.3 different eda approaches
2.4 optimization in continuous domains with edas
2.5 summary
chapter 3 an empirical comparison of edas in binary search spaces
3.1 introduction
3.2 experiments
3.3 test functions for the convergence reliability
3.4 experimental results
3.5 summary
chapter 4 development of a new type of edas based on principle of maximum entropy
4.1 introduction
4.2 entropy and schemata
4.3 the idea of the proposed algorithms
4.4 how can the estimated distribution be computed and sampled?
4.5 new algorithms
4.6 empirical results
4.7 summary
chapter 5 applying continuous edas to optimization problems
5.1 introduction
5.2 description of the optimization problems
5.3 edas to test
5.4 experimental description
5.5 summary
chapter 6 optimizing curriculum scheduling problem using eda
6.1 introduction
6.2 optimization problem of curriculum scheduling
6.3 methodology
6.4 experimental results
6.5 summary
chapter 7 recognizing human brain images using edas
7.1 introduction
7.2 graph matching problem
7.3 representing a matching as a permutation
7.4 apply edas to obtain a permutation that symbolizes the solution
7.5 obtaining a permutation with continuous edas
7.6 experimental results
7.7 summary
chapter 8 optimizing dynamic pricing problem with edas and ga
8.1 introduction
8.2 dynamic pricing for resource management
8.3 modeling dynamic pricing
8.4 an ea approaches to dynamic pricing
8.5 experiments and results
8.6 summary
chapter 9 improvement techniques of edas
9.1 introduction
9.2 tradeoffs are exploited by efficiency-improvement techniques
9.3 evaluation relaxation: designing adaptive endogenous surrogates
9.4 time continuation: mutation in edas
9.5 summary
展開全部
基于種群概率模型的優化技術-從算法到應用 節選
《基于種群概率模型的優化技術:從算法到應用(英文版)》較系統地討論了遺傳算法和分布估計算法的基本理論，并在二進制搜尋空間實驗性地比較了幾種分布估算法。在此基礎上深入地論述了構建一類新的分布估計算法的思路和實現方法，*后介紹了分布估計算法在計算機科學、資源管理等領域的一些成功應用實例及分布估計算法的幾種有效改進方法。
基于種群概率模型的優化技術-從算法到應用 相關資料
插圖：other non,binary information.For example，we might interpret a bit-string of length 80 as ten 8 bit integers.Usually this is a mistake.and better results can be obtained by using the integer or real-valued representations directly.One of the problems of coding numbers in binary is that different bits have different significance.This Can be helped by using Gray coding，which is a variation on the way that integers are mapped on bit strings.The standard method has the disadvantage that the Hamming distance between two consecutive integers is often not equal to one.If the goal is to evolve an integer number，you would like to have thechance of changing a 7 into an 8 equal to that of changing it to a 6.The chance of changing 0111 to 1000 by independent bit-flips is not the same，however，as that of changing it to 01 10.Gray coding is a representation which ensures that consecutiveintegers always have Hamming distance one.1.3.2 Integer RepresentationsBinary representations are not always the most suitable if our problem more naturally maps onto a representation where different genes can take one of a setvalues.One obvious example of when this might occur is the problem of finding the optimal values for a set of variables that all take integer values.These values might beunrestricted，or might be restricted to a finite set：for example，if we are trying toevolve a path on square grid，we might restrict the values to the rest{0，1，2，3}representing{North，East，South，West}.In either case an integer encoding isprobably more suitable than a binary encoding.'When designing the encoding andvariation operators，it is worth considering whether there are any natural relationsbetween the possible values that an attribute Can take.This might be obvious forordinal attributes such as integers，but for cardinal attributes such as the compasspoints above，there may not be a natural ordering.
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