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基于种群概率模型的优化技术-从算法到应用 版权信息
- ISBN：9787313063694
- 条形码：9787313063694 ; 978-7-313-06369-4
- 装帧：暂无
- 册数：暂无
- 重量：暂无
- 所属分类：>
基于种群概率模型的优化技术-从算法到应用 本书特色
《基于种群概率模型的优化技术:从算法到应用(英文版)》共有9个章节组成，系统地讨论了遗传算法和分布估计算法的基本理论，并在二进制搜寻空间实验性地比较了几种分布估算法。在此基础上深入地论述了构建一类新的分布估计算法的思路和实现方法，*后介绍了分布估计算法在计算机科学、资源管理等领域的一些成功应用实例。《基于种群概率模型的优化技术:从算法到应用(英文版)》可作为从事概率论、数量建模等课程研究的人员参考读物。
基于种群概率模型的优化技术-从算法到应用 内容简介
本书较系统地讨论了遗传算法和分布估计算法的基本理论，并在二进制搜寻空间实验性地比较了几种分布估算法。在此基础上深入地论述了构建一类新的分布估计算法的思路和实现方法，*后介绍了分布估计算法在计算机科学、资源管理等领域的一些成功应用实例及分布估计算法的几种有效改进方法。
基于种群概率模型的优化技术-从算法到应用 目录
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
展开全部
基于种群概率模型的优化技术-从算法到应用 节选
《基于种群概率模型的优化技术:从算法到应用(英文版)》较系统地讨论了遗传算法和分布估计算法的基本理论，并在二进制搜寻空间实验性地比较了几种分布估算法。在此基础上深入地论述了构建一类新的分布估计算法的思路和实现方法，*后介绍了分布估计算法在计算机科学、资源管理等领域的一些成功应用实例及分布估计算法的几种有效改进方法。
基于种群概率模型的优化技术-从算法到应用 相关资料
插图：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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