性别作为吉布斯抽样：进化的概率模型
Sex as Gibbs Sampling: a probability model of evolution
摘要：
我们表明，进化计算可以实现为标准的马尔可夫链蒙特卡罗（MCMC）采样。只要稍加注意，“遗传算法”就可以构造成满足详细平衡的可逆马尔可夫链;因此，种群的平稳分布是一种简单因式分解形式的吉布斯分布。对于一些标准和流行的非参数概率模型，我们展示吉布斯抽样程序，是合理的遗传算法。在突变选择均衡中，基因组群体类似于贝叶斯后验样本，基因组类似于潜在变量。我们认为这是一个通用的，易于处理的，和有见地的制定进化计算的标准机器学习的概念和技术。此外，我们表明，进化过程中，选择的行为在繁殖力的差异是不可逆的，也是不可能构建可逆的进化模型中，每个孩子只产生了两个父母。
英文摘要：
We show that evolutionary computation can be implemented as standard Markov-chain Monte-Carlo (MCMC) sampling. With some care, `genetic algorithms' can be constructed that are reversible Markov chains that satisfy detailed balance; it follows that the stationary distribution of populations is a Gibbs distribution in a simple factorised form. For some standard and popular nonparametric probability models, we exhibit Gibbs-sampling procedures that are plausible genetic algorithms. At mutation-selection equilibrium, a population of genomes is analogous to a sample from a Bayesian posterior, and the genomes are analogous to latent variables. We suggest this is a general, tractable, and insightful formulation of evolutionary computation in terms of standard machine learning concepts and techniques. In addition, we show that evolutionary processes in which selection acts by differences in fecundity are not reversible, and also that it is not possible to construct reversible evolutionary models in which each child is produced by only two parents.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
