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Found 2 papers, 1 papers with code
Tackling Neural Architecture Search With Quality Diversity Optimization
Although considerable progress has been made in the field of multi-objective NAS, we argue that there is some discrepancy between the actual optimization problem of practical interest and the optimization problem that multi-objective NAS tries to solve.
BOP-Elites, a Bayesian Optimisation algorithm for Quality-Diversity search
Quality Diversity (QD) algorithms such as MAP-Elites are a class of optimisation techniques that attempt to find a set of high-performing points from an objective function while enforcing behavioural diversity of the points over one or more interpretable, user chosen, feature functions.
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