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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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Fast Immune System Inspired Hypermutation Operators for Combinatorial Optimisation., , and . CoRR, (2020)On the benefits of populations for the exploitation speed of standard steady-state genetic algorithms., and . GECCO, page 1452-1460. ACM, (2019)Automatic adaptation of hypermutation rates for multimodal optimisation., , and . FOGA, page 4:1-4:12. ACM, (2021)On inversely proportional hypermutations with mutation potential., , and . GECCO, page 215-223. ACM, (2019)When hypermutations and ageing enable artificial immune systems to outperform evolutionary algorithms., , and . Theor. Comput. Sci., (2020)On the Benefits of Populations for the Exploitation Speed of Standard Steady-State Genetic Algorithms., and . Algorithmica, 82 (12): 3676-3706 (2020)Artificial immune systems can find arbitrarily good approximations for the NP-hard number partitioning problem., , and . Artif. Intell., (2019)On the Benefits of Populations on the Exploitation Speed of Standard Steady-State Genetic Algorithms., and . CoRR, (2019)Fast Immune System-Inspired Hypermutation Operators for Combinatorial Optimization., , and . IEEE Trans. Evol. Comput., 25 (5): 956-970 (2021)On Steady-State Evolutionary Algorithms and Selective Pressure: Why Inverse Rank-Based Allocation of Reproductive Trials Is Best., , , and . ACM Trans. Evol. Learn. Optim., 1 (1): 2:1-2:38 (2021)