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A Simple Statistical Test Against Origin-Biased Metaheuristics., and . EvoApplications@EvoStar, volume 14634 of Lecture Notes in Computer Science, page 322-337. Springer, (2024)Improving Time and Memory Efficiency of Genetic Algorithms by Storing Populations as Minimum Spanning Trees of Patches.. GECCO Companion, page 1873-1881. ACM, (2023)Can OneMax help optimizing LeadingOnes using the EA+RL method?, and . CEC, page 1762-1768. IEEE, (2015)Improved Selection of Auxiliary Objectives Using Reinforcement Learning in Non-stationary Environment., , and . ICMLA, page 580-583. IEEE, (2014)Illustrating the trade-off between time, quality, and success probability in heuristic search: a discussion paper., , , and . GECCO (Companion), page 1807-1812. ACM, (2019)Fixed-target runtime analysis of the (1 + 1) EA with resampling., , , , and . GECCO (Companion), page 2068-2071. ACM, (2019)Towards better estimation of statistical significance when comparing evolutionary algorithms.. GECCO (Companion), page 1782-1788. ACM, (2019)On binary unbiased operators returning multiple offspring., and . GECCO (Companion), page 1395-1398. ACM, (2017)From fitness landscape analysis to designing evolutionary algorithms: the case study in automatic generation of function block applications., , and . GECCO (Companion), page 1902-1905. ACM, (2018)Runtime analysis of different Approaches to select conflicting auxiliary objectives in the generalized OneMax problem., , and . SSCI, page 1-7. IEEE, (2016)