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  1. Empirical Evaluation of Evolutionary Algorithms with Power-Law Ranking Selection

    Dang, D. C., Eremeev, A. V. & Qin, X., 2024, IFIP Advances in Information and Communication Technology. Springer, p. 217-232 16 p. (IFIP Advances in Information and Communication Technology; vol. 703 IFIPAICT).

    Research output: Chapter in Book/Report/Conference proceeding › Chapter › Research › peer-review

  2. Generalization of the Heavy-Tailed Mutation in the (1+(λ,λ)) Genetic Algorithm

    Eremeev, A. & Topchii, V., 14 Jul 2024, GECCO 2024 Companion - Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion. Association for Computing Machinery, p. 93-94 2 p. (GECCO 2024 Companion - Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion).

    Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

  3. Generalized Heavy-tailed Mutation for Evolutionary Algorithms

    Eremeev, A. V., Silaev, D. V. & Topchii, V. A., 2024, In: Siberian Electronic Mathematical Reports. 21, 2, p. 940-959 20 p.

    Research output: Contribution to journal › Article › peer-review

  4. On the efficiency of non-elitist evolutionary algorithms in the case of sparsity of the level sets inconsistent with respect to the objective function

    Eremeev, A. V., 2024, In: Trudy Instituta Matematiki i Mekhaniki UrO RAN. 30, 4, p. 84-105 22 p., 7.

    Research output: Contribution to journal › Article › peer-review

  5. On the Efficiency of Nonelitist Evolutionary Algorithms in the Case of Sparsity of the Level Sets Inconsistent with Respect to the Objective Function

    Eremeev, A. V., 10 Mar 2025, In: Proceedings of the Steklov Institute of Mathematics. 327, S1, p. S91-S111 21 p.

    Research output: Contribution to journal › Article › peer-review

ID: 59828830