Paper:
An Evolutionary Algorithm for Black-Box Chance-Constrained Function Optimization
Kazuyuki Masutomi*, Yuichi Nagata**, and Isao Ono*
*Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology,
*4259 Nagatsuta-cho, Midori-ku, Yokohama 226-8502, Japan
Education Academy of Computational Life Sciences, Tokyo Institute of Technology,
**4259 Nagatsuta-cho, Midori-ku, Yokohama 226-8501, Japan
- [1] A. Charnes and W. W. Cooper, “Chance-Constrained Programming,” Manag. Sci., Vol.6, No.1, pp. 73-79, 1960.
- [2] V. Heidrich-Meisner and C. Igel, “Uncertainty Handling in Evolutionary Direct Policy Search,” NIPS-08 Workshop on Model Uncert. and Risk in RL, 2008.
- [3] L. Busoniu, R. Babuska, and B. de. Schutter, “A Comprehensive Survey ofMulti-Agent Reinforcement Learning,” IEEE Trans. Sys., Man, Cyber., Vol.38, No.2, pp. 156-172, 2008.
- [4] D. K. Pratihar, “Evolutionary robotics – A review,” Sadhana, Vol.28, No.6, pp. 999-1009, 2003.
- [5] A. Charnes and W. W. Cooper, “Deterministic equivalents for optimizing and satisfying under chance constraints,” Oper. Res., Vol.11, pp. 18-39, 1963.
- [6] W. W. Cooper, H. Hemphill, and D. Sullivan, “Survey of mathematical programming models in air pollution management,” Euro. J. Oper. Res., Vol.96, 1997.
- [7] K. Deb, S. Gupta, and D. Daum, “Reliability-Based Optimization Using Evolutionary Algorithms,” IEEE Trans. Evol. Comput., Vol.13, No.5, pp. 1054-1074, 2009.
- [8] Y. Jin and J. Branke, “Evolutionary Optimization in Uncertain Environments – A Survey,” IEEE Trans. Evol. Comput., Vol.9, No.3, pp. 303-317, 2005.
- [9] S. R. Loughlin and S. R. Ranjithan, “Chance-constrained genetic algorithms,” Proc. Genetic Evol. Comput. Conf., pp. 369-376, 1999.
- [10] L. Davis, “The Handbook of Genetic Algorithms,” Van Nostrand Reinhold, 1990.
- [11] L. Eshelman, “Real-Coded Genetic Algorithms and Interval-Schemata,” Found. Genetic Algo., Vol.2, pp. 187-202, 1993.
- [12] K. Deb, D. Joshi, and A. Anand, “Real-coded evolutionary algorithms with parent-centric recombination,” Proc. IEEE Cong. Evol. Comput., Vol.1, pp. 61-66, 2002.
- [13] C. Herväs-Martïnez, D. Ortiz-Boyer, and N. Garcïa-Pedrajas, “Theoretical Analysis of the Confidence Interval Based Crossover for Real-Coded Genetic Algorithms,” Paral. Prob. Solv. Nat. VII, pp. 153-161, 2002.
- [14] F. Herrera, M. Lozano, and A. Sanchez, “A Taxonomy for the Crossover Operator for Real-Coded Genetic Algorithms: An Experimental Study,” Int. J. Intel. Sys., Vol.18, pp. 309-338, 2003.
- [15] P. Ballester and J. Carter, “An Effective Real-Parameter Genetic Algorithm with Parent Centric Normal Crossover for Multimodal Optimisation,” Proc. Genetic Evol. Comput. Conf. Vol.3102, pp. 901-913, 2004.
- [16] J. Sakuma and S. Kobayashi, “Latent variable crossover for k-tablet structures and its application to lens design problems,” Proc. Genetic Evol. Comput. Conf., pp. 1347-1354, 2005.
- [17] H. Someya, “Promising Search Regions of Crossover Operators for Function Optimization,” Proc. 20th Int. Conf. Indust., Eng. & Other Appl. Appl. Intel. Sys., pp. 434-443, 2007.
- [18] H. Kita and M. Yamamura, “A functional specialization hypothesis for designing genetic algorithms,” Proc. IEEE SMC ’99 Conf., pp. 579-584, 1999.
- [19] S. Kobayashi, “The Frontiers of Real-Coded Genetic Algorithms,” Trans. Jpn. Soc. Artif. Intel., Vol.24, No.1, pp. 147-162, 2009 (in Japanese).
- [20] Y. Akimoto, Y. Nagata, J. Sakuma, I. Ono, and S. Kobayashi, “Analysis of the behavior of MGG and JGG as a selection model for real-coded genetic algorithms,” Trans. Jpn. Soc. Artif. Intel., Vol.25, No.2, pp. 281-289, 2010 (in Japanese).
- [21] I. Ono, S. Kobayashi, and K. Yoshida, “Global and Multi-objective Optimization for Lens Design by Real-coded Genetic Algorithms,” SPIE Proc. Int. Optical Design Conf., Vol.3482, pp. 110-121, 1998.
- [22] K. Boese, “Cost Versus Distance in the Traveling Salesman Problem,” Tech. Rep. TR-950018, UCLA CS Depart., 1995.
- [23] J. Branke, “Creating robust solutions by means of an evolutionary algorithm,” Proc. Int. Conf. Paral. Prob. Solv. Nat., pp. 119-128, 1998.
- [24] Y. Sano and H. Kita, “Optimization of noisy fitness functions by means of genetic algorithms using history of search,” Proc. Int. Conf. Paral. Prob. Solv. Nat., pp. 571-580, 2000.
- [25] J. Branke, C. Schomidt, and H. Schmeck, “Efficient Fitness Estimation in Noisy Environments,” Proc. Genetic Evol. Comput. Conf. pp. 243-250, 2001.
- [26] H. Someya, “Theoretical Parameter Value for Appropriate Population Variance of the Distribution of Children in Real-coded GA,” Proc. IEEE Cong. Evol. Comput., pp. 2722-2729, 2008.
- [27] A. J. F. van Rooij, L. C. Jain, and R. P. Johnson, “Neural Network Training Using Genetic Algorithms,” World Scientific, 1996.
- [28] G. F. Franklin, J. D. Powell, and A. Emami-Naeini, “Feedback Control of Dynamic Systems,” Pearson Prentice Hall, 6th ed., 2010.
- [29] S. Tsutsui, S. Yamamura, and T. Higuchi, “Multi-parent recombination with simplex crossover in real-coded genetic algorithms,” Proc. Genetic Evol. Comput. Conf., pp. 657-664, 1999.
This article is published under a Creative Commons Attribution 4.0 International License.