Algorithms for Optimization by Mykel J. Kochenderfer & Tim A. Wheeler

Author:Mykel J. Kochenderfer & Tim A. Wheeler [Kochenderfer, Mykel J. & Wheeler, Tim A.]
Language: eng
Format: epub

6 T. W. Athan and P. Y. Papalambros, “A Note on Weighted Criteria Methods for Compromise Solutions in Multi-Objective Optimization,” Engineering Optimization, vol. 27, no. 2, pp. 155–176, 1996.

7 Population methods are covered in chapter 9.

8 J. D. Schaffer, “Multiple Objective Optimization with Vector Evaluated Genetic Algorithms,” in International Conference on Genetic Algorithms and Their Applications, 1985.

9 K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II,” IEEE Transactions on Evolutionary Computation, vol. 6, no. 2, pp. 182–197, 2002.

10 H. Ishibuchi and T. Murata, “A Multi-Objective Genetic Local Search Algorithm and Its Application to Flowshop Scheduling,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 28, no. 3, pp. 392–403, 1998.

11 Typically the size of the population.

12 Fitness is inversely related to the objective being minimized.

13 D. E. Goldberg and J. Richardson, “Genetic Algorithms with Sharing for Multimodal Function Optimization,” in International Conference on Genetic Algorithms, 1987.

14 S. Narayanan and S. Azarm, “On Improving Multiobjective Genetic Algorithms for Design Optimization,” Structural Optimization, vol. 18, no. 2–3, pp. 146–155, 1999.

15 This chapter overviews non-Bayesian approaches to preference elicitation. For Bayesian approaches, see: S. Guo and S. Sanner, “Real-Time Multiattribute Bayesian Preference Elicitation with Pairwise Comparison Queries,” in International Conference on Artificial Intelligence and Statistics (AISTATS), 2010. J. R. Lepird, M. P. Owen, and M. J. Kochenderfer, “Bayesian Preference Elicitation for Multiobjective Engineering Design Optimization,” Journal of Aerospace Information Systems, vol. 12, no. 10, pp. 634–645, 2015.

16 V. Conitzer, “Eliciting Single-Peaked Preferences Using Comparison Queries,” Journal of Artificial Intelligence Research, vol. 35, pp. 161–191, 2009.

17 The previous weight vector may or may not be consistent with the added constraint (a(n) −b(n))⊤w < 0.

18 V. S. Iyengar, J. Lee, and M. Campbell, “Q-EVAL: Evaluating Multiple Attribute Items Using Queries,” in ACM Conference on Electronic Commerce, 2001.

19 D. Braziunas and C. Boutilier, “Elicitation of Factored Utilities,” AI Magazine, vol. 29, no. 4, pp. 79–92, 2009.

20 D. Braziunas and C. Boutilier, “Minimax Regret-Based Elicitation of Generalized Additive Utilities,” in Conference on Uncertainty in Artificial Intelligence (UAI), 2007.

21 C. Boutilier, R. Patrascu, P. Poupart, and D. Schuurmans, “Constraint-Based Optimization and Utility Elicitation Using the Minimax Decision Criterion,” Artificial Intelligence, vol. 170, no. 8–9, pp. 686–713, 2006.

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