EAs-from-the-papers

Evolutionary algorithms from the papers

Analysis & Convergence Analysis

1. Natural Evolution Strategies Converge on Sphere Functions. GECCO '12. [paper, reference]

   Tom Schaul.

2. Learning Rate Adaptation by Line Search in Evolution Strategies with Recombination. GECCO '22. [paper, appendix, reference]

   Armand Gissler, Anne Auger, Nikolaus Hansen.

3. Analysis of Evolution Strategies with the Optimal Weighted Recombination. GECCO '18. [paper, reference]

   Chun-kit Au, Ho-fung Leung.

4. Analysis of Information Geometric Optimization with Isotropic Gaussian Distribution Under Finite Samples. GECCO '18. [paper, reference]

   Kento Uchida, Shinichi Shirakawa, Youhei Akimoto.

5. Reconsidering the Progress Rate Theory for Evolution Strategies in Finite Dimensions. GECCO '06. [paper, reference]

   Anne Auger, Nikolaus Hansen.

6. Convergence Rates of Efficient Global Optimization Algorithms. JMLR vol. 12, 2011. [paper, reference]

   Adam D. Bull.

7. Towards a Stronger Theory for Permutation-based Evolutionary Algorithms. GECCO '22. [paper, reference]

   Benjamin Doerr, Yassine Ghannane, Marouane Ibn Brahim.

8. Convergence Rate of the (1+1)-Evolution Strategy with Success-Based Step-Size Adaptation on Convex Quadratic Functions. GECCO '21. [paper, reference]

   Daiki Morinaga, Kazuto Fukuchi, Jun Sakuma, Youhei Akimoto.

9. Simple algorithms for optimization on Riemannian manifolds with constraints. Applied Mathematics & Optimization, vol. 82, 2020. [paper, reference]

   Changshuo Liu, Nicolas Boumal.

10. Globally convergent evolution strategies. Mathematical Programming, vol. 152. [paper, reference]

    Y. Diouane, S. Gratton, L. N. Vicente.

11. On Proving Linear Convergence of Comparison-based Step-size Adaptive Randomized Search on Scaling-Invariant Functions via Stability of Markov Chains. INRIA, 2013. [paper, reference]

    Anne Auger, Nikolaus Hansen.

12. Convergence Analysis of Optimization Algorithms. arXiv. [paper, reference]

    HyoungSeok Kim, JiHoon Kang, WooMyoung Park, SukHyun Ko, YoonHo Cho, DaeSung Yu, YoungSook Song and JungWon Choi.

13. Convergence Analysis. Lecture. [document]

14. Numerical Optimization. Class note. [document]

15. The Benefits and Limitations of Voting Mechanisms in Evolutionary Optimisation. FOGA '19. [paper, reference]

    Jonathan E. Rowe, Aishwaryaprajna.

16. Convergence Analysis of Differential Evolution Variants on Unconstrained Global Optimization Functions. IJAIA, vol. 2, 2011. [paper, reference]

    G.Jeyakumar, C.Shanmugavelayutham.

Classical Evolution Strategies

1. The Dynamics of Cumulative Step-Size Adaptation on the Ellipsoid Model. Evolutionary Computation, vol. 24, 2016. [paper, reference]

   Hans-Georg Beyer, Michael Hellwig.

2. Global linear convergence of Evolution Strategies with recombination on scaling-invariant functions. Journal of Global Optimization, vol. 86, 2023. [paper, reference]

   Cheikh Toure, Anne Auger, Nikolaus Hansen.

3. Log-linear Convergence of the Scale-invariant (µ/µw, λ)-ES and Optimal µ for Intermediate Recombination for Large Population Sizes. PPSN '10. [paper, reference]

   Mohamed Jebalia, Anne Auger.

4. On a Population Sizing Model for Evolution Strategies Optimizing the Highly Multimodal Rastrigin Function. GECCO '23. [paper, reference]

   Lisa Schönenberger, Hans-Georg Beyer.

5. Self-Adaptation of Multi-Recombinant Evolution Strategies on the Highly Multimodal Rastrigin Function. Evolutionary Compatation, 2024. [paper, reference]

   Amir Omeradzic, Hans-Georg Beyer.

6. The Dynamics of Self-Adaptive Multi-Recombinant Evolution Strategies on the General Ellipsoid Model. Evolutionary Computation, vol. 18, 2014. [paper, reference]

   Hans-Georg Beyer, Alexander Melkozerov.

7. Self-Adaptation in Evolution Strategies. Thesis. [document, reference]

   Silja Meyer-Nieberg.

8. The Theory of Evolution Strategies. Book. [document, reference]

   Hans-Georg Beyer.

9. Markov chain Analysis of Evolution Strategies. Thesis. [document, reference]

   Alexandre Chotard.