Algorithm Modelization: Transforming Optimization Algorithms into Evolvable Intelligence

Key points

• Can algorithms be learned and evolved as models, rather than repeatedly redesigned as programs?1
• As a first realization of this paradigm, we modelize iterative optimization as an autoregressive process, where preceding generations constitute the computational context for generating the next population.1
• We develop a dimension-independent population-temporal architecture to capture both population interactions and iterative dynamics, together with an imitation-to-surpassing learning framework that first acquires algorithmic knowledge from heterogeneous teachers and then improves the resulting optimizer directly through optimization feedback.1
• Our results establish a promising path from manually designed algorithms toward general algorithm models that learn, generalize, and evolve from computational experience.1

Sources (1)

• [1]Algorithm Modelization: Transforming Optimization Algorithms into Evolvable Intelligence
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 16, 08:59 AM

  We explore this question by introducing Algorithm Modelization, a new paradigm that turns iterative algorithms into trainable models.

  Can algorithms be learned and evolved as models, rather than repeatedly redesigned as programs?

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