基于種群概率模型的優(yōu)化技術(shù)

前言

　　The study and use of population-based probabilistic modeling techniques for optimization have been successfully developed during the last decade.Among thesete chniques.Genetic algorithms（GAS）and Estimation of Distribution Algorithms（EDAS）have been the reference.　　This book，comprised of a total of 9 chapters，covers broadly important spectrum subjects ranging from fundamental theories of GAS and EDAS，development of a new type of EDAs and applications of EDAS to efficiency enhancements of EDAs.Inchapter 1，GA fundamentals are discussed. We begin with what is usually the most critical decision in any application，namely that of deciding how best a candidate solution is represented to the algorithm.We then describe variation operators suitablefor different types of representation，before turning our attention to the selection andreplacement mechanisms that are used to manage the populations of solutions.Inchapter 2.the EDAS proposed for the solution of combinatorial optimization problems and optimization in continuous domains are reviewed.Different approaches for EDAS have been ordered by the complexity of interrelations SO that they are able to express.An empirical comparison of EDAS in binary search spaces is covered in chapter 3.Furthermore，techniques of implementations of a new type of EDAS are studied in chapter 4.The experimental results of applying EDAS to some optimization problems are shown in chapter 5.Chapter 6，7 and 8 bring together some EDAs approaches to optimization problems in the fields of graph matching and resource management.Finally，chapter 9 provides an overview of different efficiency-enhancement techniques for EDAS.　　This book should be of interested to theoreticians and practitioners alike.and iS a must-have resources for those interested in optimization in general，and genetics and estimation of distribution algorithms in particular.Also engineers who，in their dailylife，face real.

內(nèi)容概要

本書較系統(tǒng)地討論了遺傳算法和分布估計算法的基本理論，并在二進(jìn)制搜尋空間實驗性地比較了幾種分布估算法。在此基礎(chǔ)上深入地論述了構(gòu)建一類新的分布估計算法的思路和實現(xiàn)方法，最后介紹了分布估計算法在計算機(jī)科學(xué)、資源管理等領(lǐng)域的一些成功應(yīng)用實例及分布估計算法的幾種有效改進(jìn)方法。

書籍目錄

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  SummaryChapter 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  SummaryChapter 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  SummaryChapter 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  SummaryChapter 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  SummaryChapter 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  SummaryChapter 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  SummaryChapter 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  SummaryChapter 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

章節(jié)摘錄

　　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 Representations　　Binary 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.　　1.3.3　Real-Valued Representations　　Often the most sensible way to candidate solution to a problem is to have a stringof real values.This occurs when the values that we want to represent as genes comefrom a continuous rather than a discrete distribution.Of course，on a computer theprecision of these real values is actually limited by the implementation.

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