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Reinforcement learning based dynamic selection of auxiliary objectives with preservation of the best found solution., and . GECCO (Companion), page 1435-1438. ACM, (2017)NSGA-II implementation details may influence quality of solutions for the job-shop scheduling problem., , and . GECCO (Companion), page 1445-1446. ACM, (2014)Generation of tests for programming challenge tasks using multi-objective optimization., , and . GECCO (Companion), page 1655-1658. ACM, (2013)Selecting evolutionary operators using reinforcement learning: initial explorations., , and . GECCO (Companion), page 1033-1036. ACM, (2014)Fitness-Dependent Hybridization of Clonal Selection Algorithm and Random Local Search., , and . GECCO (Companion), page 5-6. ACM, (2016)Adaptive Selection of Helper-Objectives with Reinforcement Learning., and . ICMLA (2), page 66-67. IEEE, (2012)978-1-4673-4651-1.Runtime analysis of a population-based evolutionary algorithm with auxiliary objectives selected by reinforcement learning., , and . GECCO (Companion), page 1886-1889. ACM, (2018)Selection of Auxiliary Objectives in the Travelling Salesman Problem using Reinforcement Learning., and . GECCO (Companion), page 1455-1456. ACM, (2015)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)