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International audienceThe evolvability of a system is the ability to generate heritable, novel and non-lethal phenotypes, from random genetic mutations. However, most evolutionary computation studies estimate evolvability either as, (i) the proportion of mutations beneficial to an individual’s performance, irrespective of the phenotypic diversity of the mutants, or (ii) the range and diverseness of mutated phenotypes, without taking into account the viability of the genetic change. This paper reports a novel approach to measure the evolvability provided by an encoding, by characterizing both the quality of the mutations and the quantity of phenotypic variation. We evolved controllers for hexapod robot locomotion using a parameterized direct encoding, and the generative encoding of artificial neural net- works (similar to HyperNEAT) and single-unit pattern generators (SUPGs). Our results reveal that the performance of an encoding is not always a good assessment of evolvability. Although both the generative encodings evaluated had individuals with high performance gaits, there were appar- ent differences in their measured evolvability. A direct and predictive relationship is indicated between our measure of evolvability, and the number of generations required by amputated individuals to recover an effective gait

Comparing the Evolvability of Generative Encoding Schemes