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Fast Immune System-Inspired Hypermutation Operators for Combinatorial Optimization.

, , и . IEEE Trans. Evol. Comput., 25 (5): 956-970 (2021)

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Pietro Cortona

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Tutorials at PPSN 2016., , , , , , , , , и 19 other автор(ы). PPSN, том 9921 из Lecture Notes in Computer Science, стр. 1012-1022. Springer, (2016)Rigorous Performance Analysis of Hyper-heuristics.. Automated Design of Machine Learning and Search Algorithms, Springer, (2021)On the Time Complexity of Algorithm Selection Hyper-Heuristics for Multimodal Optimisation., , и . AAAI, стр. 2322-2329. AAAI Press, (2019)How Theoretical Analyses Can Impact Practical Applications of Evolutionary Computation.. IJCCI, стр. 5. ScitePress, (2019)Runtime analysis of population-based evolutionary algorithms., и . GECCO Companion, стр. 1398-1426. ACM, (2022)Artificial immune systems can find arbitrarily good approximations for the NP-hard number partitioning problem., , и . Artif. Intell., (2019)Computational Complexity Analysis of Genetic Programming., и . Theory of Evolutionary Computation, Springer, (2020)Tight Bounds on the Expected Runtime of a Standard Steady State Genetic Algorithm., , и . Algorithmica, 84 (6): 1603-1658 (2022)On the Benefits of Populations for the Exploitation Speed of Standard Steady-State Genetic Algorithms., и . Algorithmica, 82 (12): 3676-3706 (2020)When hypermutations and ageing enable artificial immune systems to outperform evolutionary algorithms., , и . Theor. Comput. Sci., (2020)