基于种群的进化优化元学习目标
Population-Based Evolution Optimizes a Meta-Learning Objective
摘要：
元学习模型，或者说学会学习的模型，由于其快速解决新任务的能力，一直是人们渴望已久的目标。传统的元学习方法可能需要昂贵的内部和外部循环，因此需要发现强大学习者而无需显式搜索它们的算法。我们借鉴了进化系统中可进化基因组的研究--具有强大适应能力的基因组--并提出元学习和自适应可进化性针对相同的目标进行优化：在一组学习迭代后获得高性能。我们认为，基于种群的进化系统与非静态的健身景观自然偏向于高进化性基因组，因此优化具有较强的学习能力的人口。我们用一个简单的进化算法来证明这一点，基于人口的Meta学习（PBML），该算法不断发现基因组，这些基因组在几代人中表现出更高的改进率，并且可以快速适应解决稀疏健身和机器人控制任务。
英文摘要：
Meta-learning models, or models that learn to learn, have been a long-desired target for their ability to quickly solve new tasks. Traditional meta-learning methods can require expensive inner and outer loops, thus there is demand for algorithms that discover strong learners without explicitly searching for them. We draw parallels to the study of evolvable genomes in evolutionary systems -- genomes with a strong capacity to adapt -- and propose that meta-learning and adaptive evolvability optimize for the same objective: high performance after a set of learning iterations. We argue that population-based evolutionary systems with non-static fitness landscapes naturally bias towards high-evolvability genomes, and therefore optimize for populations with strong learning ability. We demonstrate this claim with a simple evolutionary algorithm, Population-Based Meta Learning (PBML), that consistently discovers genomes which display higher rates of improvement over generations, and can rapidly adapt to solve sparse fitness and robotic control tasks.
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。
