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DAE-GP: denoising autoencoder LSTM networks as probabilistic models in estimation of distribution genetic programming.

, , and . GECCO, page 1037-1045. ACM, (2020)

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The Influence of Probabilistic Grammars on Evolution., , , and . GECCO Companion, page 611-614. ACM, (2023)Pretraining Reduces Runtime in Denoising Autoencoder Genetic Programming by an Order of Magnitude., , and . GECCO Companion, page 2382-2385. ACM, (2023)Program synthesis with evolutionary algorithms: Status quo: hot off the press track (GECCO 2022)., , and . GECCO Companion, page 39-40. ACM, (2022)DAE-GP: denoising autoencoder LSTM networks as probabilistic models in estimation of distribution genetic programming., , and . GECCO, page 1037-1045. ACM, (2020)Recent Developments in Program Synthesis with Evolutionary Algorithms., , and . CoRR, (2021)On the Influence of Grammars on Crossover in Grammatical Evolution.. EuroGP, volume 12691 of Lecture Notes in Computer Science, page 114-129. Springer, (2021)Exploiting Knowledge from Code to Guide Program Search., , , and . EuroGP, volume 13223 of Lecture Notes in Computer Science, page 262-277. Springer, (2022)A Comprehensive Survey on Program Synthesis With Evolutionary Algorithms., , and . IEEE Trans. Evol. Comput., 27 (1): 82-97 (February 2023)Effects of the Training Set Size: A Comparison of Standard and Down-Sampled Lexicase Selection in Program Synthesis., , and . CEC, page 1-8. IEEE, (2022)On sampling error in genetic programming., , and . Nat. Comput., 21 (2): 173-186 (2022)