Quality-Diversity (QD) algorithms are a new type of Evolutionary Algorithms (EAs), aiming to
find a set of high-performing, yet diverse solutions. They have found many successful …

How people spend their finite time budget of 24 hours on daily activities is linked to their
wellbeing. Yet, how to best allocate time to optimise multi-dimensional wellbeing (physical …

Computing diverse solutions for a given problem, in particular evolutionary diversity
optimisation (EDO), is a hot research topic in the evolutionary computation community. This …

The evolutionary diversity optimization aims at finding a diverse set of solutions which satisfy
some constraint on their fitness. In the context of multi-objective optimization this constraint …

Quality diversity (QD) is a branch of evolutionary computation that gained increasing interest
in recent years. The Map-Elites QD approach defines a feature space, ie, a partition of the …

Optimal layout problems consist in positioning a given number of components in order to
minimize an objective function while satisfying geometrical or functional constraints. Such …

Quality Diversity (QD) aims to evolve a population of solutions that are both diverse and of
high quality. The Map-Elites QD approach partitions the search space according to a feature …

Diversity optimisation explores a variety of solutions for the intended problem and is rapidly
growing and getting more popular within the evolutionary computation community as a …

The field of Multidisciplinary Design and Optimization (MDO) methods introduce a wide
range of optimization techniques able to generate innovative preliminary design solutions …