Algorithm Modelization: Turning algorithms into evolvable models
In brief
- Algorithms are modeled as trainable models, not fixed programs.
- A population-temporal architecture captures iterative dynamics and algorithm interactions.
- The method improves optimizers by consolidating heterogeneous knowledge and generalizing to unseen problems.
A new paradigm proposes to model algorithms as trainable models rather than fixed programs. Called 'Algorithm Modelization', this approach learns and improves algorithmic behaviors from execution data. It paves the way for more powerful and generalizable optimizers that integrate heterogeneous knowledge.
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