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One of the most important lessons from the success of deep learning is thatlearned representations tend to perform much better at any task compared torepresentations we design by hand. Yet evolution of evolvability algorithms,which aim to automatically learn good genetic representations, have receivedrelatively little attention, perhaps because of the large amount ofcomputational power they require. The recent method Evolvability ES allowsdirect selection for evolvability with little computation. However, it can onlybe used to solve problems where evolvability and task performance are aligned.We propose Quality Evolvability ES, a method that simultaneously optimizes fortask performance and evolvability and without this restriction. Our proposedapproach Quality Evolvability has similar motivation to Quality Diversityalgorithms, but with some important differences. While Quality Diversity aimsto find an archive of diverse and well-performing, but potentially geneticallydistant individuals, Quality Evolvability aims to find a single individual witha diverse and well-performing distribution of offspring. By doing so QualityEvolvability is forced to discover more evolvable representations. Wedemonstrate on robotic locomotion control tasks that Quality Evolvability ES,similarly to Quality Diversity methods, can learn faster than objective-basedmethods and can handle deceptive problems.

Quality Evolvability ES: Evolving Individuals With a Distribution of Well Performing and Diverse Offspring