nips nips2011 nips2011-50 nips2011-50-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Javad Azimi, Alan Fern, Xiaoli Z. Fern
Abstract: Budgeted optimization involves optimizing an unknown function that is costly to evaluate by requesting a limited number of function evaluations at intelligently selected inputs. Typical problem formulations assume that experiments are selected one at a time with a limited total number of experiments, which fail to capture important aspects of many real-world problems. This paper defines a novel problem formulation with the following important extensions: 1) allowing for concurrent experiments; 2) allowing for stochastic experiment durations; and 3) placing constraints on both the total number of experiments and the total experimental time. We develop both offline and online algorithms for selecting concurrent experiments in this new setting and provide experimental results on a number of optimization benchmarks. The results show that our algorithms produce highly effective schedules compared to natural baselines. 1
[1] B. S. Anderson, A. Moore, and D. Cohn. A nonparametric approach to noisy and costly optimization. In ICML, 2000.
[2] J. Azimi, A. Fern, and X. Fern. Batch bayesian optimization via simulation matching. In NIPS, 2010.
[3] D. Bond and D. Lovley. Electricity production by geobacter sulfurreducens attached to electrodes. Applications of Environmental Microbiology, 69:1548–1555, 2003.
[4] E. Brochu, M. Cora, and N. de Freitas. A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning. Technical Report TR-2009-23, Department of Computer Science, University of British Columbia, 2009.
[5] E. H. Burrows, W.-K. Wong, X. Fern, F. W. Chaplen, and R. L. Ely. Optimization of ph and nitrogen for enhanced hydrogen production by synechocystis sp. pcc 6803 via statistical and machine learning methods. Biotechnology Progress, 25:1009–1017, 2009.
[6] H. Chang, R. Givan, and E. Chong. Parallel rollout for online solution of partially observable markov decision processes. Discrete Event Dynamic Systems, 14:309–341, 2004.
[7] L. Dixon and G. Szeg. The Global Optimization Problem: An Introduction Toward Global Optimization. NorthHolland, Amsterdam, 1978.
[8] D. Jones. A taxonomy of global optimization methods based on response surfaces. Journal of Global Optimization, pages 345–383, 2001.
[9] Z. Michalewicz. Genetic algorithms + data structures = evolution programs (2nd, extended ed.). Springer-Verlag New York, Inc., New York, NY, USA, 1994.
[10] D. Park and J. Zeikus. Improved fuel cell and electrode designs for producing electricity from microbial degradation. Biotechnol.Bioeng., 81(3):348–355, 2003. 9