title: Efficient Computing Budget Allocation for Finding Simplest Good Designs.
abstract: In many applications some designs are easier to implement, require less training data and shorter training time, and consume less storage than the others. Such designs are called simple designs, and are usually preferred over complex ones when they all have good performance. Despite the abundant existing studies on how to find good designs in simulation-based optimization (SBO), there exist few studies on finding simplest good designs. We consider this important problem in this paper, and make the following contributions. First, we provide lower bounds for the probabilities of correctly selecting themsimplest designs with top performance, and selecting the bestmsuch simplest good designs, respectively. Second, we develop two efficient computing budget allocation methods to findmsimplest good designs and to find the bestmsuch designs, respectively; and show their asymptotic optimalities. Third, we compare the performance of the two methods with equal allocations over 6 academic examples and a smoke detection problem in wireless sensor networks. We hope that this work brings insight to finding the simplest good designs in general.
authors.affiliation: Center for Intelligent and Networked Systems (CFINS), Department of Automation, TNLIST, Tsinghua University, Beijing 100084, China (Tel.: +86-10-62773006, fax: +86-10-62796115.
authors.forename: Qing-Shan
authors.initials: QS
authors.lastname: Jia
authors.forename: Enlu
authors.initials: E
authors.lastname: Zhou
authors.forename: Chun-Hung
authors.initials: CH
authors.lastname: Chen
keywords: Simulation-based optimization; complexity preference; optimal computing budget allocation; wireless sensor network