A genetic algorithm (GA) is a search-based optimization technique based on the principles of Genetics and Natural Selection. We present an algorithm which enhances the classical GA with input from quantum annealers. As in a classical GA, the algorithm works by breeding a population of possible solutions based on their fitness. However, the population of individuals is defined by the continuous couplings on the quantum annealer, which then give rise via quantum annealing to the set of corresponding phenotypes that represent attempted solutions. This introduces a form of directed mutation into the algorithm that can enhance its performance in various ways. Two crucial enhancements come from the continuous couplings having strengths that are inherited from the fitness of the parents (so-called nepotism) and from the annealer couplings allowing the entire population to be influenced by the fittest individuals (so-called quantum-polyandry). We find our algorithm to be significantly more powerful on several simple problems than a classical GA.
翻译:基因算法(GA) 是一种基于遗传学和自然选择原则的基于搜索的优化技术。 我们展示了一种利用量子射精器的投入增强古典GA的算法。 和古典GA一样,算法通过培养以其身体健康为基础的可能解决办法而产生的结果。 然而,个人群体的定义是通过量子射精器上连续的组合来界定的,然后通过量子射精产生成一组代表尝试中的解决办法的对应的体型。 这在能够以各种方式提高它的性能的算法中引入了一种定向突变形式。 两种关键的增强来自具有长处的连续组合,这些组合具有从父母的体格(所谓的任者主义)所继承的长处和使整个人口受到适者(所谓的量子射精度射精度)影响而成的Anneal联结。 我们发现我们的算法在几个简单的问题上比典型的GA要强大得多。