text: 381,113,1370563872168374275,1665897810,Kevin Frans,"New paper on how Population-based Evolution is a natural meta-learning algorithm! With @okw at @crosslabstokyo <LINK> @okw @crosslabstokyo Basic idea: In evolutionary algorithms, a strong gene is a gene which survives for many generations. Thus, strong genes should grant fitness not only to an individual but also to all of the individual's offspring. This means that in non-stationary environments, genes that increase the *adaptive ability* of a genome are naturally selected for. Over time, the learning ability of a population increases. This perspective can help explain why biological systems are so adaptable, or why languages are so robust -- with large populations, evolution naturally selects for systems that are good at adapting to new tasks.",https://arxiv.org/abs/2103.06435,"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. ",Population-Based Evolution Optimizes a Meta-Learning Objective,4,"['New paper on how Population-based Evolution is a natural meta-learning algorithm! With @okw at @crosslabstokyo <LINK>', ""@okw @crosslabstokyo Basic idea: In evolutionary algorithms, a strong gene is a gene which survives for many generations. Thus, strong genes should grant fitness not only to an individual but also to all of the individual's offspring."", 'This means that in non-stationary environments, genes that increase the *adaptive ability* of a genome are naturally selected for. Over time, the learning ability of a population increases.', 'This perspective can help explain why biological systems are so adaptable, or why languages are so robust -- with large populations, evolution naturally selects for systems that are good at adapting to new tasks.']",21,03,755