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Hyperschemas Theory for GP with One-Point crossover, Building Blocks, and some New Results in GA Theory

机译:Hyperschemas理论为GP的GP,构建块以及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架构定理的两个主要弱点是它们仅提供关于在下一代E [M(H,T + 1)]的给定模式的情况的预期值的信息,并且它们只能给出一个下限为这样的数量。本文提出了关于GP和GA模式的新理论结果,这在很大程度上克服了这些弱点。首先,与以前的结果集中在赛事生存和中断,我们的结果延伸到GP近期对GA理论的工作,并通过斯蒂芬和Waelbroeck进行GA理论,并制定了架构创建的效果和机制。这使我们能够为下一个世代属的架构的预期实例提供精确的配方(而不是下限)。由于这种配方,我们能够为早期的GP架构定理提供改进版本,其中占了一些模式创建事件,因此Botainign为E [M(H,T + 1)]的更严格绑定。这界限是架构本身的选择概率以及一组低阶模式的函数,其中单点交叉用于构建架构的实例。该恢复支持GP中的构建块的存在,Howeve,R不一定是全部短,低位或高度适合。在早期的工作中,我们展示了斯蒂芬斯和Waelbroecks的GA结果和本文中描述的新GP结果可用于评估模式方差,信噪比,通常,M(H, T + 1)。此外,我们展示了如何从架构定理中删除期望运算符,以便以已知的概率预测M(h,t + _1)(而不是e [m(h,t + 1))进行高于给定的阈值。

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