What is heterogeneous ensemble optimization?

Heterogeneous ensemble optimization finds the best combination of different model types and weights.

Heterogeneous ensemble optimization involves selecting and tuning diverse models (trees, linear models, SVMs, neural networks) and determining optimal weights for combining them. Optimization techniques include grid search, Bayesian optimization, evolutionary algorithms, and stacking-based learning. The goal is to maximize predictive performance while maintaining generalization.