There has been a growing interest in the evolutionary computation community
to compute a diverse set of high-quality solutions for a given optimisation
problem. This can provide the practitioners with invaluable information about
the solution space and robustness against imperfect modelling and minor
problems' changes. It also enables the decision-makers to involve their
interests and choose between various solutions. In this study, we investigate
for the first time a prominent multi-component optimisation problem, namely the
Traveling Thief Problem (TTP), in the context of evolutionary diversity
optimisation. We introduce a bi-level evolutionary algorithm to maximise the
structural diversity of the set of solutions. Moreover, we examine the
inter-dependency among the components of the problem in terms of structural
diversity and empirically determine the best method to obtain diversity. We
also conduct a comprehensive experimental investigation to examine the
introduced algorithm and compare the results to another recently introduced
framework based on the use of Quality Diversity (QD). Our experimental results
show a significant improvement of the QD approach in terms of structural
diversity for most TTP benchmark instances.Comment: To appear at GECCO 202
