Landscape-Sketch-Step: An AI/ML-Based Metaheuristic for Surrogate Optimization Problems
Rafael Monteiro, Kartik Sau

TL;DR
This paper presents Landscape-Sketch-and-Step (LSS), a novel AI/ML-based metaheuristic for efficient global optimization that reduces the number of costly function evaluations without constructing explicit surrogate models.
Contribution
LSS combines machine learning, stochastic optimization, and reinforcement learning to improve global optimization efficiency, especially in high-cost evaluation scenarios, without surrogate model reconstruction.
Findings
LSS accelerates optimization compared to classical methods.
LSS requires a similar number of evaluations as Simulated Annealing.
LSS effectively handles rugged energy landscapes in low-dimensional problems.
Abstract
In this paper, we introduce a new heuristics for global optimization in scenarios where extensive evaluations of the cost function are expensive, inaccessible, or even prohibitive. The method, which we call Landscape-Sketch-and-Step (LSS), combines Machine Learning, Stochastic Optimization, and Reinforcement Learning techniques, relying on historical information from previously sampled points to make judicious choices of parameter values where the cost function should be evaluated at. Unlike optimization by Replica Exchange Monte Carlo methods, the number of evaluations of the cost function required in this approach is comparable to that used by Simulated Annealing, quality that is especially important in contexts like high-throughput computing or high-performance computing tasks, where evaluations are either computationally expensive or take a long time to be performed. The method also…
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Taxonomy
TopicsAdvanced Multi-Objective Optimization Algorithms · Advanced Bandit Algorithms Research · Machine Learning and Data Classification
