Merging real-valued GOMEA with GP-GOMEA enables simultaneous optimization of constants and expression structure, generally outperforming other constant-handling techniques in symbolic regression.
InProceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO ’22)
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Domain experts require fast convergence and some explainability from evolutionary algorithms in physics-informed optimization, with other needs varying by problem, revealing an application gap.
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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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Performance and Explainability Requirements of Evolutionary Algorithms in Real-World Physics-Informed Optimization
Domain experts require fast convergence and some explainability from evolutionary algorithms in physics-informed optimization, with other needs varying by problem, revealing an application gap.