首页> 外文会议>European Conference on Genetic Programming EuroGP 2000 Edinburgh, Scotland, UK, April 15-16, 2000 >Hyperschemas Theory for GP with One-Point crossover, Building Blocks, and some New Results in GA Theory
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Hyperschemas Theory for GP with One-Point crossover, Building Blocks, and some New Results in GA Theory

机译:一站式交叉,积木式GP的Hyperschemas理论和GA理论的一些新结果

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Two main weaknesses of GA and GP schema theorems are that they provide only information on the expected value of the number of instances of a given schema at the next generation E[m(H,t+1)], and they can only give a lower bound for such a quantity. this paper presents new theoreticalresults on GP and GA schemata which largely overcome these weaknesses. Firstly, unlike previous results which concentrated on schema survival and disruption, our results extend to GP recent work on GA theory by Stephens and Waelbroeck, and make the effects and the mechanisms of schema creation explicit. This allows us to give an exact formulation (rather than a lower bound) for the expected number of instances of a schema at the next generaiton. Thanks to this formulation we are then able to provide in improved version for an earlier GP schema theorem in which some schema creation events are accounted for, thus botainign a tighter bound for E[m(H,t+1)]. This bound is a function of the selection probabilities of the schema itself and of a set of lower-order schemata which one-point crossover uses to build instances of the schema. This reult supports the existance of building blocks in GP which ,howeve,r are not necessarily all short, low-order or highly fit. Building on earlier work, we show how Stephens and Waelbroecks' GA results and the new GP results described in the paper can be used to evalaute schema variance, signal-to-noise ratio and, in general, the probability distribution of m(H, t+1). In addition, we show how the expectation operator can be removed from the schema theorem so as to predict with a known probability whether m(H, t+_1) (rather than E[m(H,t+1)]) is going to be above a given threshold.
机译:GA和GP模式定理的两个主要缺点是,它们仅提供有关下一代E [m(H,t + 1)]上给定模式实例数的预期值的信息,并且它们只能给出这样的数量的下限。本文介绍了有关GP和GA模式的新理论结果,这些结果在很大程度上克服了这些缺点。首先,与以前的结果集中在模式生存和破坏上不同,我们的结果扩展到GP最近由Stephens和Waelbroeck提出的关于遗传算法的工作,并明确了模式创建的作用和机理。这使我们能够为下一个世代的模式实例的预期数量给出精确的公式(而不是下限)。多亏了这种表述,我们才能为早期的GP模式定理提供改进版本,其中考虑了一些模式创建事件,从而使E [m(H,t + 1)]的界限更严格。此界限是模式本身和一组低阶模式的选择概率的函数,单点交叉用于构建模式实例。该结果支持GP中构建块的存在,然而,构建块不一定都是短的,低阶的或高度合适的。在早期工作的基础上,我们展示了Stephens和Waelbroecks的遗传算法结果以及本文中描述的新GP结果如何可用于评估方案方差,信噪比以及总体上m(H, t + 1)。另外,我们展示了如何从模式定理中删除期望算子,从而以已知的概率预测m(H,t + _1)(而不是E [m(H,t + 1)])高于给定的阈值。

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