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Optimizing residential energy resources with an improved multi-objective genetic algorithm based on greedy mutations.

, , and . GECCO, page 1246-1253. ACM, (2018)

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Arbitrarily Close Alignments in the Error Space: A Geometric Semantic Genetic Programming Approach., , , and . GECCO (Companion), page 99-100. ACM, (2016)Random Sampling Technique for Overfitting Control in Genetic Programming., , , and . EuroGP, volume 7244 of Lecture Notes in Computer Science, page 218-229. Springer, (2012)A quantitative assessment of the effect of different algorithmic schemes to the task of learning the structure of Bayesian Networks., , , and . CoRR, (2017)On the Generalization Ability of Geometric Semantic Genetic Programming., , and . EuroGP, volume 9025 of Lecture Notes in Computer Science, page 41-52. Springer, (2015)Prediction of Forest Aboveground Biomass: An Exercise on Avoiding Overfitting., , , , , , , , and . EvoApplications, volume 7835 of Lecture Notes in Computer Science, page 407-417. Springer, (2013)Semantic learning machine improves the CNN-Based detection of prostate cancer in non-contrast-enhanced MRI., , , and . GECCO (Companion), page 1837-1845. ACM, (2019)Unsure when to stop?: ask your semantic neighbors., , , and . GECCO, page 929-936. ACM, (2017)Improving eQTL Analysis Using a Machine Learning Approach for Data Integration: A Logistic Model Tree Solution., , , , , and . J. Comput. Biol., 25 (10): 1091-1105 (2018)Explorations of the Semantic Learning Machine Neuroevolution Algorithm: Dynamic Training Data Use, Ensemble Construction Methods, and Deep Learning Perspectives., , and . GPTP, page 39-62. Springer, (2019)Optimizing residential energy resources with an improved multi-objective genetic algorithm based on greedy mutations., , and . GECCO, page 1246-1253. ACM, (2018)