@inproceedings{48894,
  abstract     = {{Recently different evolutionary computation approaches have been developed that generate sets of high quality diverse solutions for a given optimisation problem. Many studies have considered diversity 1) as a mean to explore niches in behavioural space (quality diversity) or 2) to increase the structural differences of solutions (evolutionary diversity optimisation). In this study, we introduce a co-evolutionary algorithm to simultaneously explore the two spaces for the multi-component traveling thief problem. The results show the capability of the co-evolutionary algorithm to achieve significantly higher diversity compared to the baseline evolutionary diversity algorithms from the literature.}},
  author       = {{Nikfarjam, Adel and Neumann, Aneta and Bossek, Jakob and Neumann, Frank}},
  booktitle    = {{Parallel Problem Solving from Nature (PPSN XVII)}},
  editor       = {{Rudolph, Günter and Kononova, Anna V. and Aguirre, Hernán and Kerschke, Pascal and Ochoa, Gabriela and Tu\v sar, Tea}},
  isbn         = {{978-3-031-14714-2}},
  keywords     = {{Co-evolutionary algorithms, Evolutionary diversity optimisation, Quality diversity, Traveling thief problem}},
  pages        = {{237–249}},
  publisher    = {{Springer International Publishing}},
  title        = {{{Co-Evolutionary Diversity Optimisation for the Traveling Thief Problem}}},
  doi          = {{10.1007/978-3-031-14714-2_17}},
  year         = {{2022}},
}