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PRETSL: Distributed Probabilistic Rule Evolution for Time-Series Classification.

, , , , and . GPTP, page 139-148. Springer, (2016)

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PRETSL: Distributed Probabilistic Rule Evolution for Time-Series Classification., , , , and . GPTP, page 139-148. Springer, (2016)Distributed Age-Layered Novelty Search., , and . ALIFE, page 131-138. MIT Press, (2016)Enhanced Optimization with Composite Objectives and Novelty Pulsation., , , , , , , and . GPTP, page 275-293. Springer, (2019)nPool: Massively Distributed Simultaneous Evolution and Cross-Validation in EC-Star., and . GPTP, page 79-90. Springer, (2015)Enhanced Optimization with Composite Objectives and Novelty Selection., , and . ALIFE, page 616-622. MIT Press, (2018)How to select a winner in evolutionary optimization?, , , and . SSCI, page 1-6. IEEE, (2017)Regularized evolutionary population-based training., , , and . GECCO, page 323-331. ACM, (2021)Evolving explainable rule sets., , and . GECCO Companion, page 1779-1784. ACM, (2022)Maintenance of a Long Running Distributed Genetic Programming System for Solving Problems Requiring Big Data., , , and . GPTP, page 65-83. Springer, (2013)Estimating the Advantage of Age-Layering in Evolutionary Algorithms., , and . GECCO, page 693-699. ACM, (2016)