Merging real-valued GOMEA with GP-GOMEA enables simultaneous optimization of constants and expression structure, generally outperforming other constant-handling techniques in symbolic regression.
In: Proceedings of the Genetic and Evolutionary Computation Conference
2 Pith papers cite this work. Polarity classification is still indexing.
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Enhanced Baymex with parallelization and adaptive steering yields statistically similar or better classification performance than decision trees, logistic regression, naive Bayes and random forests on clinical data while returning multiple compact, inspectable Bayesian networks.
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Simultaneous Model-Based Evolution of Constants and Expression Structure in GP-GOMEA for Symbolic Regression
Merging real-valued GOMEA with GP-GOMEA enables simultaneous optimization of constants and expression structure, generally outperforming other constant-handling techniques in symbolic regression.
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Parallel Adaptive Multi-Objective Evolutionary Learning of Discretized Bayesian Network Classifiers for Clinical Data
Enhanced Baymex with parallelization and adaptive steering yields statistically similar or better classification performance than decision trees, logistic regression, naive Bayes and random forests on clinical data while returning multiple compact, inspectable Bayesian networks.