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Theoretical-Computer-Science_TCS.md

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TCS (Theoretical Computer Science)

  • Bienvenüe, A. and François, O., 2003. Global convergence for evolution strategies in spherical problems: Some simple proofs and difficulties. Theoretical Computer Science, 306(1-3), pp.269-289.
  • Omeradzic, A. and Beyer, H.G., 2023. Progress analysis of a multi-recombinative evolution strategy on the highly multimodal Rastrigin function. Theoretical Computer Science, 978, p.114179. [ www ]
  • Bossek, J. and Sudholt, D., 2023. Do additional target points speed up evolutionary algorithms?. Theoretical Computer Science, 950, p.113757. [ www ]
  • Doerr, B. and Rajabi, A., 2023. Stagnation detection meets fast mutation. Theoretical Computer Science, 946, p.113670. [ www ]
  • Hellwig, M. and Beyer, H.G., 2020. On the steady state analysis of covariance matrix self-adaptation evolution strategies on the noisy ellipsoid model. Theoretical Computer Science, 832, pp.98-122. [ www ]
  • Akimoto, Y., Auger, A. and Hansen, N., 2020. Quality gain analysis of the weighted recombination evolution strategy on general convex quadratic functions. Theoretical Computer Science, 832, pp.42-67. [ www ] ( ES )
  • Shir, O.M. and Yehudayoff, A., 2020. On the covariance-hessian relation in evolution strategies. Theoretical Computer Science, 801, pp.157-174. [ www ] ( ES )
  • Echegoyen, C., Santana, R., Mendiburu, A. and Lozano, J.A., 2015. Comprehensive characterization of the behaviors of estimation of distribution algorithms. Theoretical Computer Science, 598, pp.64-86. [ www ]
  • Hellwig, M. and Beyer, H.G., 2016. Mutation strength control via meta evolution strategies on the ellipsoid model. Theoretical Computer Science, 623, pp.160-179. [ www ]
  • Doerr, B. and Künnemann, M., 2015. Optimizing linear functions with the (1+ λ) evolutionary algorithm—different asymptotic runtimes for different instances. Theoretical Computer Science, 561, pp.3-23. [ www ]
  • Jansen, T. and Zarges, C., 2014. Performance analysis of randomised search heuristics operating with a fixed budget. Theoretical Computer Science, 545, pp.39-58. [ www ]
  • Bringmann, K. and Friedrich, T., 2012. Approximating the least hypervolume contributor: NP-hard in general, but fast in practice. Theoretical Computer Science, 425, pp.104-116.
  • Finck, S. and Beyer, H.G., 2012. Performance analysis of the simultaneous perturbation stochastic approximation algorithm on the noisy sphere model. Theoretical Computer Science, 419, pp.50-72. [ www ]
  • Jansen, T. and Wegener, I., 2007. A comparison of simulated annealing with a simple evolutionary algorithm on pseudo-boolean functions of unitation. Theoretical Computer Science, 386(1-2), pp.73-93. [ www ]
  • Arnold, D.V., 2006. Weighted multirecombination evolution strategies. Theoretical Computer Science, 361(1), pp.18-37. [ www ] ( ES | Continuous Optimization )
  • Jägersküpper, J., 2006. How the (1+1) ES using isotropic mutations minimizes positive definite quadratic forms. Theoretical Computer Science, 361(1), pp.38-56. [ www ] ( ES | Continuous Optimization )
    • Jägersküpper, J., 2003, June. Analysis of a simple evolutionary algorithm for minimization in Euclidean spaces. In ICALP (pp. 1068-1079).
  • Auger, A., 2005. Convergence results for the (1, λ)-SA-ES using the theory of ϕ-irreducible Markov chains. Theoretical Computer Science, 334(1-3), pp.35-69. [ www ]
  • Dorigo, M. and Blum, C., 2005. Ant colony optimization theory: A survey. Theoretical Computer Science, 344(2-3), pp.243-278. [ www ] ( ACO )
  • Fischer, S. and Wegener, I., 2005. The one-dimensional Ising model: Mutation versus recombination. Theoretical Computer Science, 344(2-3), pp.208-225. [ www ]
  • Beyer, H.G., Schwefel, H.P. and Wegener, I., 2002. How to analyse evolutionary algorithms. Theoretical Computer Science, 287(1), pp.101-130. [ www ]
  • Droste, S., Jansen, T. and Wegener, I., 2002. On the analysis of the (1+1) evolutionary algorithm. Theoretical Computer Science, 276(1-2), pp.51-81. [ www ]
  • Droste, S., Jansen, T. and Wegener, I., 2002. Optimization with randomized search heuristics—the (A) NFL theorem, realistic scenarios, and difficult functions. Theoretical Computer Science, 287(1), pp.131-144. [ www ]
  • Eiben, A.E. and Rudolph, G., 1999. Theory of evolutionary algorithms: A bird's eye view. Theoretical Computer Science, 229(1-2), pp.3-9. [ www ]