@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}},
}

@inproceedings{48876,
  abstract     = {{In recent years, Evolutionary Algorithms (EAs) have frequently been adopted to evolve instances for optimization problems that pose difficulties for one algorithm while being rather easy for a competitor and vice versa. Typically, this is achieved by either minimizing or maximizing the performance difference or ratio which serves as the fitness function. Repeating this process is useful to gain insights into strengths/weaknesses of certain algorithms or to build a set of instances with strong performance differences as a foundation for automatic per-instance algorithm selection or configuration. We contribute to this branch of research by proposing fitness-functions to evolve instances that show large performance differences for more than just two algorithms simultaneously. As a proof-of-principle, we evolve instances of the multi-component Traveling Thief Problem (TTP) for three incomplete TTP-solvers. Our results point out that our strategies are promising, but unsurprisingly their success strongly relies on the algorithms’ performance complementarity.}},
  author       = {{Bossek, Jakob and Wagner, Markus}},
  booktitle    = {{Proceedings of the Genetic and Evolutionary Computation Conference Companion}},
  isbn         = {{978-1-4503-8351-6}},
  keywords     = {{evolutionary algorithms, evolving instances, fitness function, instance hardness, traveling thief problem (TTP)}},
  pages        = {{1423–1432}},
  publisher    = {{Association for Computing Machinery}},
  title        = {{{Generating Instances with Performance Differences for More than Just Two Algorithms}}},
  doi          = {{10.1145/3449726.3463165}},
  year         = {{2021}},
}

@inproceedings{48852,
  abstract     = {{The Traveling Salesperson Problem (TSP) is one of the best-known combinatorial optimisation problems. However, many real-world problems are composed of several interacting components. The Traveling Thief Problem (TTP) addresses such interactions by combining two combinatorial optimisation problems, namely the TSP and the Knapsack Problem (KP). Recently, a new problem called the node weight dependent Traveling Salesperson Problem (W-TSP) has been introduced where nodes have weights that influence the cost of the tour. In this paper, we compare W-TSP and TTP. We investigate the structure of the optimised tours for W-TSP and TTP and the impact of using each others fitness function. Our experimental results suggest (1) that the W-TSP often can be solved better using the TTP fitness function and (2) final W-TSP and TTP solutions show different distributions when compared with optimal TSP or weighted greedy solutions.}},
  author       = {{Bossek, Jakob and Neumann, Aneta and Neumann, Frank}},
  booktitle    = {{Parallel Problem Solving from Nature (PPSN XVI)}},
  isbn         = {{978-3-030-58111-4}},
  keywords     = {{Evolutionary algorithms, Node weight dependent TSP, Traveling Thief Problem}},
  pages        = {{346–359}},
  publisher    = {{Springer-Verlag}},
  title        = {{{Optimising Tours for the Weighted Traveling Salesperson Problem and the Traveling Thief Problem: A Structural Comparison of Solutions}}},
  doi          = {{10.1007/978-3-030-58112-1_24}},
  year         = {{2020}},
}