@article{46371,
  abstract     = {{One main task in evolutionary multiobjective optimization (EMO) is to obtain a suitable finite size approximation of the Pareto front which is the image of the solution set, termed the Pareto set, of a given multiobjective optimization problem. In the technical literature, the characteristic of the desired approximation is commonly expressed by closeness to the Pareto front and a sufficient spread of the solutions obtained. In this paper, we first make an effort to show by theoretical and empirical findings that the recently proposed Averaged Hausdorff (or Δ𝑝-) indicator indeed aims at fulfilling both performance criteria for bi-objective optimization problems. In the second part of this paper, standard EMO algorithms combined with a specialized archiver and a postprocessing step based on the Δ𝑝 indicator are introduced which sufficiently approximate the Δ𝑝-optimal archives and generate solutions evenly spread along the Pareto front.}},
  author       = {{Rudolph, G and Schütze, O and Grimme, C and Domínguez-Medina, C and Trautmann, Heike}},
  journal      = {{Computational Optimization and Applications (Comput. Optim. Appl.)}},
  number       = {{2}},
  pages        = {{589–618}},
  title        = {{{Optimal averaged Hausdorff archives for bi-objective problems: theoretical and numerical results}}},
  doi          = {{10.1007/s10589-015-9815-8}},
  volume       = {{64}},
  year         = {{2016}},
}