Combining Gaussian processes, mutual information and a genetic algorithm for multi-target optimization of expensive-to-evaluate functions

Engineering Optimization

Volumn 46, Issue 11, 2014, Pages 1593-1607

  Peremezhney, N ^a   Hines, E ^a   Lapkin, A ^b   Connaughton, C ^a  

^a UNIVERSITY OF WARWICK   (United Kingdom)

^b UNIVERSITY OF CAMBRIDGE   (United Kingdom)

Author keywords

experimental design; hypervolume indicator; information gain; multi target optimization

Indexed keywords

ALGORITHMS; BUDGET CONTROL; DESIGN OF EXPERIMENTS; GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC); GENETIC ALGORITHMS; OPTIMAL SYSTEMS; PARETO PRINCIPLE;

GAUSSIAN PROCESSES; HYPERVOLUME INDICATORS; INFORMATION GAIN; MULTI-TARGET OPTIMIZATION; MUTUAL INFORMATIONS; ON-LINE EVOLUTIONARY ALGORITHMS; PARETO OPTIMAL SOLUTIONS; SYNTHETIC PROBLEM;

FUNCTION EVALUATION;

EID: 84905399451     PISSN: 0305215X     EISSN: 10290273     Source Type: Journal    
DOI: 10.1080/0305215X.2014.881997     Document Type: Article

References (14)

1. Abramowitz, M., and I. A. Stegun. 1965. Handbook of Mathematical Functions, 84-85. NewYork: Dover.
2. Cox, D., and S. John. 1997. "A Statistical Method for Global Optimization." In Multidisciplinary Design Optimization: State of the Art, edited by N. Alexandrow and M. Hussaini, 315-329. Philadelphia, PA: SIAM.
3. Deb, K., A. Pratap, S. Agarwal, and T. Meyarivan. 2002. "A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II." IEEE Transactions on Evolutionary Computation 6 (2): 182-197.
4. El-Beltagy, M. A., and A. J. Keane. 2001. "Evolutionary Optimization for Computationally Expensive Problems Using Gaussian Processes." In Proceedings of the International Conference on Artificial Intelligence, 708-714.: CSREA Press.
5. Emmerich, M., K. Giannakoglou, and B. Naujoks. 2006. "Single and Multiobjective Evolutionary Optimization Assisted by Gaussian Rrandom Field Metamodels." IEEE Transactions on Evolutionary Computation 10 (4): 421-439.
6. Guestrin, C., A. Krause, and A. Singh. 2005. "Near-Optimal Sensor Placements in Gaussian Processes." In International Conference on Machine Learning (ICML), Bonn, Germany, August 7-11.
7. Harrington, J. 1965. "The Desirability Function." Industrial Quality Control 21 (10): 494-498.
8. Jones, D., M. Schonlau, andW.Welch. 1998. "Efficient Global Optimization of Expensive Black-Box Functions." Journal of Global Optimization 13 (4): 455-492.
9. Liu, B., Q. Zhang, and G. Gielen. 2013. "A Gaussian Process Surrogate Model Assisted Evolutionary Algorithm for Medium Scale Expensive Optimization Problems." IEEE Transactions on Evolutionary Computation, PP (99). doi:10.1109/TEVC.2013.2248012.
10. McLachlan, G., and D. Peel. 2000. Finite Mixture Models. Hoboken, NJ:Wiley.
11. Peremezhney, N., C. Connaughton, G. Unali, E. Hines, and A. A. Lapkin. 2012. "Application of Dimensionality Reduction to Visualisation of High-Throughput Data and Building of a Classification Model in Formulated Consumer Product Design." Chemical Engineering Research and Design 90 (12): 2179-2185.
12. Rasmussen, C. E., and C. K. I.Williams. 2006. Gaussian Processes for Machine Learning. Cambridge, MA: MIT Press.
13. Wenzel, S., S. Straatmann, L. Kwiatkowski, P. Schmelzer, and J. Kunert. 2010. "A Novel Multi-Objective Target Value Optimization Approach." In Classification as a Tool for Research: Studies in Classification, Data Analysis, and Knowledge Organization, edited by H. Locarek-Junge and C.Weihs, 801-809, Berlin: Springer-Verlag.
14. Zitzler, E., and L. Thiele. 1998. "Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study." In Parallel Problem Solving From Nature, V, edited by A. E. Eiben, T. Bäck, M. Schoenauer, and H. P. Schwefel, 292-301. Berlin: Springer-Verlag.