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* 上述方法的组合

* 上述方法的组合

== The building block hypothesis ==

==构件假设 The building block hypothesis ==

Genetic algorithms are simple to implement, but their behavior is difficult to understand. In particular, it is difficult to understand why these algorithms frequently succeed at generating solutions of high fitness when applied to practical problems. The building block hypothesis (BBH) consists of:

Genetic algorithms are simple to implement, but their behavior is difficult to understand. In particular, it is difficult to understand why these algorithms frequently succeed at generating solutions of high fitness when applied to practical problems. The building block hypothesis (BBH) consists of:

# A description of a heuristic that performs adaptation by identifying and recombining "building blocks", i.e. low order, low defining-length [[Schema (genetic algorithms)|schemata]] with above average fitness.

# A description of a heuristic that performs adaptation by identifying and recombining "building blocks", i.e. low order, low defining-length [[Schema (genetic algorithms)|schemata]] with above average fitness.

# A hypothesis that a genetic algorithm performs adaptation by implicitly and efficiently implementing this heuristic.

# A hypothesis that a genetic algorithm performs adaptation by implicitly and efficiently implementing this heuristic.

 

:"Short, low order, and highly fit schemata are sampled, [[crossover (genetic algorithm)|recombined]] [crossed over], and resampled to form strings of potentially higher fitness. In a way, by working with these particular schemata [the building blocks], we have reduced the complexity of our problem; instead of building high-performance strings by trying every conceivable combination, we construct better and better strings from the best partial solutions of past samplings.

:"Short, low order, and highly fit schemata are sampled, [[crossover (genetic algorithm)|recombined]] [crossed over], and resampled to form strings of potentially higher fitness. In a way, by working with these particular schemata [the building blocks], we have reduced the complexity of our problem; instead of building high-performance strings by trying every conceivable combination, we construct better and better strings from the best partial solutions of past samplings.

:“对短的、d低次序的和高度适合的模式进行取样、重组(交叉) ，并重新取样以形成具有潜在更高适合度的表示串。在某种程度上，通过使用这些特定的模式[构建块] ，我们降低了问题的复杂性; 我们不是通过尝试每一种可以想象的组合来构建高性能表示串，而是从过去采样的最佳部分解决方案中构建越来越好的表示串。

 

“对短的、低阶的和高度适合的模式进行取样、重组[交叉] ，并重新取样以形成具有潜在更高适合度的字符串。在某种程度上，通过使用这些特定的模式[构建块] ，我们降低了问题的复杂性; 我们不是通过尝试每一种可以想象的组合来构建高性能字符串，而是从过去采样的最佳部分解决方案中构建越来越好的字符串。

:"Because highly fit schemata of low defining length and low order play such an important role in the action of genetic algorithms, we have already given them a special name: building blocks. Just as a child creates magnificent fortresses through the arrangement of simple blocks of wood, so does a genetic algorithm seek near optimal performance through the juxtaposition of short, low-order, high-performance schemata, or building blocks."{{sfn|Goldberg|1989|p=41}}

:"Because highly fit schemata of low defining length and low order play such an important role in the action of genetic algorithms, we have already given them a special name: building blocks. Just as a child creates magnificent fortresses through the arrangement of simple blocks of wood, so does a genetic algorithm seek near optimal performance through the juxtaposition of short, low-order, high-performance schemata, or building blocks."{{sfn|Goldberg|1989|p=41}}

“由于低定义长度和低次序的高度拟合图式在遗传算法中起着如此重要的作用，我们已经给它们起了一个特殊的名字: 积木。就像一个孩子通过简单的木块排列来建造宏伟的堡垒一样，遗传算法也通过并列短小、低阶、高性能的图式或积木来寻求接近最优的性能。”

:“由于低定义长度和低次序的高度拟合模式在遗传算法中起着如此重要的作用，我们已经给它们起了一个特殊的名字: 构件（building block）。就像一个孩子通过简单的木块排列来建造宏伟的堡垒一样，遗传算法也通过并列短小、低阶、高性能的图式或积木来寻求接近最优的性能。”{{sfn|Goldberg|1989|p=41}}

Despite the lack of consensus regarding the validity of the building-block hypothesis, it has been consistently evaluated and used as reference throughout the years. Many estimation of distribution algorithms, for example, have been proposed in an attempt to provide an environment in which the hypothesis would hold. Although good results have been reported for some classes of problems, skepticism concerning the generality and/or practicality of the building-block hypothesis as an explanation for GAs efficiency still remains. Indeed, there is a reasonable amount of work that attempts to understand its limitations from the perspective of estimation of distribution algorithms.

Despite the lack of consensus regarding the validity of the building-block hypothesis, it has been consistently evaluated and used as reference throughout the years. Many estimation of distribution algorithms, for example, have been proposed in an attempt to provide an environment in which the hypothesis would hold. Although good results have been reported for some classes of problems, skepticism concerning the generality and/or practicality of the building-block hypothesis as an explanation for GAs efficiency still remains. Indeed, there is a reasonable amount of work that attempts to understand its limitations from the perspective of estimation of distribution algorithms.

尽管对于构件假设的有效性缺乏共识，但多年来一直对其进行评价，并将其作为参考。例如，已经提出了许多分布估计算法，试图提供一个假设成立的环境。虽然在某些类别的问题上取得了良好的结果，但是对于构件假设作为气体效率解释的普遍性和/或实用性仍然存在怀疑。事实上，有相当数量的工作试图从分布算法估计的角度来理解其局限性。

尽管对于构件假设的有效性缺乏共识，但多年来它一直在被评测，并被作为参考。例如，已经有许多'''分布估计算法 estimation of distribution algorithms'''被提出，试图提供一个使假设成立的环境<ref>{{cite book|last1=Harik|first1=Georges R.|last2=Lobo|first2=Fernando G.|last3=Sastry|first3=Kumara|title=Linkage Learning via Probabilistic Modeling in the Extended Compact Genetic Algorithm (ECGA)|journal=Scalable Optimization Via Probabilistic Modeling|volume=33|date=1 January 2006|pages=39–61|doi=10.1007/978-3-540-34954-9_3|language=en|series=Studies in Computational Intelligence|isbn=978-3-540-34953-2}}</ref><ref>{{cite book|last1=Pelikan|first1=Martin|last2=Goldberg|first2=David E.|last3=Cantú-Paz|first3=Erick|title=BOA: The Bayesian Optimization Algorithm|journal=Proceedings of the 1st Annual Conference on Genetic and Evolutionary Computation - Volume 1|date=1 January 1999|pages=525–532|url=http://dl.acm.org/citation.cfm?id=2933973|isbn=9781558606111|series=Gecco'99}}</ref>。虽然在某些类别的问题上已经有了良好的结果，但是用构件假设来解释遗传算法有效性的普遍性和/或实用性仍然存在怀疑。事实上，有相当数量的工作试图从分布估计算法的角度来理解其局限性。

 

 

== Limitations ==

== Limitations ==