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.

A Memory-Based RASH Optimizer / Brunato, Mauro; Battiti, Roberto; Pasupuleti, Srinivas. - ELETTRONICO. - (2006), pp. 1-16.

A Memory-Based RASH Optimizer

Brunato, Mauro;Battiti, Roberto;Pasupuleti, Srinivas
2006-01-01

Abstract

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.
2006
Trento
Università degli Studi di Trento - Dipartimento di Informatica e Telecomunicazioni
A Memory-Based RASH Optimizer / Brunato, Mauro; Battiti, Roberto; Pasupuleti, Srinivas. - ELETTRONICO. - (2006), pp. 1-16.
Brunato, Mauro; Battiti, Roberto; Pasupuleti, Srinivas
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/358020
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