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Artificial immune systems can find arbitrarily good approximations for the NP-hard number partitioning problem.

, , and . Artif. Intell., (2019)

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Runtime analysis of population-based evolutionary algorithms., and . GECCO Companion, page 1398-1426. ACM, (2022)Rigorous Performance Analysis of Hyper-heuristics.. Automated Design of Machine Learning and Search Algorithms, Springer, (2021)Tutorials at PPSN 2016., , , , , , , , , and 19 other author(s). PPSN, volume 9921 of Lecture Notes in Computer Science, page 1012-1022. Springer, (2016)On the Time Complexity of Algorithm Selection Hyper-Heuristics for Multimodal Optimisation., , and . AAAI, page 2322-2329. AAAI Press, (2019)How Theoretical Analyses Can Impact Practical Applications of Evolutionary Computation.. IJCCI, page 5. ScitePress, (2019)Simplified Drift Analysis for Proving Lower Bounds in Evolutionary Computation., and . Algorithmica, 59 (3): 369-386 (2011)Analysis of population-based evolutionary algorithms for the vertex cover problem., , and . IEEE Congress on Evolutionary Computation, page 1563-1570. IEEE, (2008)Artificial immune systems can find arbitrarily good approximations for the NP-hard number partitioning problem., , and . Artif. Intell., (2019)When hypermutations and ageing enable artificial immune systems to outperform evolutionary algorithms., , and . Theor. Comput. Sci., (2020)Tight Bounds on the Expected Runtime of a Standard Steady State Genetic Algorithm., , and . Algorithmica, 84 (6): 1603-1658 (2022)