A Memory-Based RASH Optimizer

Brunato, Mauro and Battiti, Roberto and Pasupuleti, Srinivas (2006) A Memory-Based RASH Optimizer. UNSPECIFIED. (Unpublished)

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    This paper presents a memory-based Reactive Affine Shaker (M-RASH) algorithm for global optimization. The Reactive Affine Shaker is an adaptive search algorithm based only on the function values. M-RASH is an extension of RASH in which good starting points to RASH are suggested online by using Bayesian Locally Weighted Regression (B-LWR). Both techniques use the memory about the previous history of the search to guide the future exploration but in very different ways. RASH compiles the previous experience into a local search area where sample points are drawn, while locally-weighted regression saves the entire previous history to be mined extensively when an additional sample point is generated. Because of the high computational cost related to the B-LWR model, it is applied only to evaluate the potential of an initial point for a local search run. The experimental results, focussed onto the case when the dominant computational cost is the evaluation of the target $f$ function, show that M-RASH is indeed capable of leading to good results for a smaller number of function evaluations.

    Item Type: Departmental Technical Report
    Department or Research center: Information Engineering and Computer Science
    Subjects: Q Science > QA Mathematics > QA063 Problem solving
    Uncontrolled Keywords: Local Search, Reactive Search, Optimization Heuristics, Linearly Weighted Regression
    Report Number: DIT-06-023
    Repository staff approval on: 28 Apr 2006

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