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Exploring the coevolution of predator and prey morphology and behavior.

, , , , and . ALIFE, page 250-257. MIT Press, (2016)

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Pareto Inspired Multi-objective Rule Fitness for Noise-Adaptive Rule-Based Machine Learning., , and . PPSN, volume 9921 of Lecture Notes in Computer Science, page 514-524. Springer, (2016)Considerations for automated machine learning in clinical metabolic profiling: Altered homocysteine plasma concentration associated with metformin exposure., , , , , , , , and . PSB, page 460-471. (2018)A heuristic method for simulating open-data of arbitrary complexity that can be used to compare and evaluate machine learning methods., , , and . PSB, page 259-267. (2018)A System for Accessible Artificial Intelligence., , , , , , , , , and . GPTP, page 121-134. Springer, (2017)Flies as Ship Captains? Digital Evolution Unravels Selective Pressures to Avoid Collision in Drosophila., , and . ALIFE, page 554-561. MIT Press, (2016)Predator confusion is sufficient to evolve swarming behaviour, , , , and . Journal of The Royal Society Interface, (2013)Benchmarking relief-based feature selection methods for bioinformatics data mining., , , , and . J. Biomed. Informatics, (2018)Evolved digital ecosystems: Dynamic steady state, not optimal fixed point., , , , , , and . ECAL, page 126-133. MIT Press, (2013)Layered TPOT: Speeding up Tree-based Pipeline Optimization., , and . AutoML@PKDD/ECML, volume 1998 of CEUR Workshop Proceedings, page 49-68. CEUR-WS.org, (2017)Identifying and Harnessing the Building Blocks of Machine Learning Pipelines for Sensible Initialization of a Data Science Automation Tool., and . GPTP, page 211-223. Springer, (2016)