Book Chapters

  1. Schütze, O., Uribe, L., & Lara, A. (2020). The Gradient Subspace Approximation and Its Application to Bi-objective Optimization Problems. Advances in Dynamics, Optimization and Computation, 355–390. https://doi.org/10.1007/978-3-030-51264-4_15

  2. Trujillo, L., Z-Flores, E., Juárez-Smith, P. S., Legrand, P., Silva, S., Castelli, M., Vanneschi, L., Schütze, O., & Muñoz, L. (2018). Local Search is Underused in Genetic Programming. Genetic and Evolutionary Computation, 119–137.
    https://doi.org/10.1007/978-3-319-97088-2_8

  3. Hernández, C., Schütze, O., & Sun, J. Q. (2017). Global Multi-objective Optimization by Means of Cell Mapping Techniques. EVOLVE – A Bridge between Probability, Set Oriented Numerics and Evolutionary Computation VII, 25–56.
    https://doi.org/10.1007/978-3-319-49325-1_2
  4. Qin, Z. C., Xiong, F. R., Sardahi, Y., Naranjani, Y., Schütze, O., & Sun, J. Q. (2016). Multi-objective Optimal Design of Nonlinear Controls. Studies in Computational Intelligence, 205–222. 
    https://doi.org/10.1007/978-3-319-44003-3_9

  5. Adrián Sosa Hernández, V., Lara, A., Trautmann, H., Rudolph, G., & Schütze, O. (2016). The Directed Search Method for Unconstrained Parameter Dependent Multi-objective Optimization Problems. Studies in Computational Intelligence, 281–330.
    https://doi.org/10.1007/978-3-319-44003-3_12

  6. Dibene, J. C., Maldonado, Y., Vera, C., Trujillo, L., de Oliveira, M., & Schütze, O. (2016). The Ambulance Location Problem in Tijuana, Mexico. Studies in Computational Intelligence, 409–441.
    https://doi.org/10.1007/978-3-319-44003-3_17

  7. Arias Montao, A., Coello Coello, C. A., & Schtüze, O. (2014). Multiobjective Optimization for Space Mission Design Problems. Computational Intelligence in Aerospace Sciences, 1–46.
    https://doi.org/10.2514/5.9781624102714.0001.0046

  8. Salomon, S., Domínguez-Medina, C., Avigad, G., Freitas, A., Goldvard, A., Schütze, O., & Trautmann, H. (2014). PSA Based Multi Objective Evolutionary Algorithms. EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation III, 233–259.
    https://doi.org/10.1007/978-3-319-01460-9_11

  9. Schütze, O., Witting, K., Ober-Blöbaum, S., & Dellnitz, M. (2013). Set Oriented Methods for the Numerical Treatment of Multiobjective Optimization Problems. EVOLVE- A Bridge between Probability, Set Oriented Numerics and Evolutionary Computation, 187–219.
    https://doi.org/10.1007/978-3-642-32726-1_5

  10. Lara, A., Schütze, O., & Coello Coello, C. A. (2013). On Gradient-Based Local Search to Hybridize Multi-objective Evolutionary Algorithms. EVOLVE- A Bridge between Probability, Set Oriented Numerics and Evolutionary Computation, 305–332.
    https://doi.org/10.1007/978-3-642-32726-1_9

  11. Dellnitz, M., & Schütze, O. (2012). Multilevel Subdivision Techniques for Scalar Optimization Problems. Global Analysis of Nonlinear Dynamics, 221–252.
    https://doi.org/10.1007/978-1-4614-3128-2_10