  Computing diverse sets of high quality solutions for a given optimization
problem has become an important topic in recent years. In this paper, we
introduce a coevolutionary Pareto Diversity Optimization approach which builds
on the success of reformulating a constrained single-objective optimization
problem as a bi-objective problem by turning the constraint into an additional
objective. Our new Pareto Diversity optimization approach uses this
bi-objective formulation to optimize the problem while also maintaining an
additional population of high quality solutions for which diversity is
optimized with respect to a given diversity measure. We show that our standard
co-evolutionary Pareto Diversity Optimization approach outperforms the recently
introduced DIVEA algorithm which obtains its initial population by generalized
diversifying greedy sampling and improving the diversity of the set of
solutions afterwards. Furthermore, we study possible improvements of the Pareto
Diversity Optimization approach. In particular, we show that the use of
inter-population crossover further improves the diversity of the set of
solutions.
