Archive of Useful Solutions for Directed Mating in Evolutionary Constrained Multiobjective Optimization
Minami Miyakawa, Keiki Takadama, and Hiroyuki Sato
The University of Electro-Communications, 1-5-1 Chofugaoka, Chofu, Tokyo 182-8585, Japan
-  K. Deb, “Multi-Objective Optimization using Evolutionary Algorithms,” John Wiley & Sons, 2001.
-  E. Mezura-Montes, “Constraint-Handling in Evolutionary Optimization,” Springer, 2009.
-  F. Hoffmeister and J. Sprave, “Problem-independent handling of constraints by use of metric penalty functions,” Proc. of the 5th Annual Conf. on Evolutionary Programming (EP 1996), pp. 289-294, 1996.
-  T. Bäck, F. Hoffmeister, and H. Schwefel, “A Survey of Evolution Strategies,” Proc. of the 4th Int. Conf. on Genetic Algorithms, pp. 2-9, 1991.
-  C. A. C. Coello and A. D. Christiansen, “MOSES: AMultiobjective Optimization Tool for Engineering Design,” Engineering Optimization, Vol.31, No.3, pp. 337-368, 1999.
-  E. Zitzler and L. Thiele, “Multiobjective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach,” IEEE Trans. on Evolutionary Computation, Vol.3, No.4, pp. 257-271, 1999.
-  H. Ishibuchi and S. Kaige, “Effects of Repair Procedures on the Performance of EMO Algorithms for Multiobjective 0/1 Knapsack Problems,” Proc. of the 2003 Congress on Evolutionary Computation (CEC’2003), Vol.4, pp. 2254-2261, 2003.
-  A. Homaifar, S. H. Y. Lai, and X. Qi, “Constrained Optimization via Genetic Algorithms,” Trans. of The Society for Modeling and Simulation Int. – SIMULATION, Vol.62, No.4, pp. 242-254, 1994.
-  J. Joines and C. Houck, “On the Use of Non-Stationary Penalty Functions to Solve Nonlinear Constrained Optimization Problems with Gas,” Proc. of the First IEEE Conf. on Evolutionary Computation, pp. 579-584, 1994.
-  R. Farmani and J. A. Wright, “Self-Adaptive Fitness Formulation for Constrained Optimization,” IEEE Trans. on Evolutionary Computation, Vol.7, No.5, pp. 445-455, 2003.
-  K. Deb, “Evolutionary Algorithms forMulti-Criterion Optimization in Engineering Design,” Evolutionary Algorithms in Engineering and Computer Science, Chapter 8, pp. 135-161, JohnWiley & Sons, 1999.
-  J. Hazra and A. K. Sinha, “A multi-objective optimal power flow using particle swarm optimization,” European Trans. on Electrical Power, Vol.21, Issue 1, pp. 1028-1045, 2011.
-  Y. G.Woldesenbet, G. G. Yen, and B. G. Tessema, “Constraint Handling in Multiobjective Evolutionary Optimization,” IEEE Trans. on Evolutionary Computation, Vol.13, Issue 3, pp. 514-525, 2009.
-  E. Mezura-Montes and C. A. C. Coello, “Constrained Optimization via Multiobjective Evolutionary Algorithms,” Multiobjective Problem Solving from Nature, Part I, Springer, pp. 53-75, 2008.
-  K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A Fast and Elitist Multi-Objective Genetic Algorithm: NSGA-II,” IEEE Trans. on Evolutionary Computation, Vol.6, pp. 182-197, 2002.
-  T. Ray, K. Tai, and C. Seow, “An evolutionary algorithm for multiobjective optimization,” Eng. Optim., Vol.33, No.3, pp. 399-424, 2001.
-  M. Miyakawa and H. Sato, “An Evolutionary Algorithm using Twostage Non-dominated Sorting and Directed Mating for Constrained Multi-objective Optimization,” Proc. of the 6th Int. Conf. on Soft Computing and Intelligent Systems and the 13th Int. Symposium on Advanced Intelligent Systems (SCIS-ISIS2012), pp. 1441-1446, 2012.
-  M.Miyakawa and H. Sato, “Constrained Multi-Objective Optimization Using Two-Stage Non-Dominated Sorting and Directed Mating,” Trans. of the Japanese Society for Evolutionary Computation, Vol.3, No.3, pp. 185-196, 2012 (Japanese).
-  M. Miyakawa, K. Takadama, and H. Sato, “Two-Stage Non-Dominated Sorting and Directed Mating for Solving Problems with Multi-Objectives and Constraints,” Proc. of 2013 Genetic and Evolutionary Computation Conference (GECCO 2013), pp. 647-654, 2013.
-  H. Kellerer, U. Pferschy, and D. Pisinger, “Knapsack Problems,” Springer, 2004.
-  S. Kukkonen and J. Lampinen, “Constrained Real-Parameter Optimization with Generalized Differential Evolution,” Proc. of 2006 IEEE Congress on Evolutionary Computation (CEC2006), pp. 911-918, 2006.
-  E. Zitzler, “Evolutionary Algorithms for Multiobjective Optimization: Methods and Applications,” Ph.D. thesis, Swiss Federal Institute of Technology, Zurich, 1999.
-  J. Knowles and D. Corne, “On Metrics for Comparing Nondominated Sets,” Proc. 2002 IEEE Congress on Evolutionary Computation, pp. 711-716, 2002.
-  T. T. Binh and U. Korn, “MOBES: A multiobjective evolution strategy for constrained optimization problems,” Proc. of the 3rd Int. Conf. on Genetic Algorithms (Mendel 97), pp. 176-182, 1997.
-  A. Osyczka and S. Kundu, “A new method to solve generalized multicriteria optimization problems using the simple genetic algorithm,” Structural Optimization, Vol.10, No.2, pp. 94-99, 1995.
-  M. Tanaka, H. Watanabe, Y. Furukawa, and T. Tanino, “GA-Based Decision Support System for Multicriteria Optimization,” Proc of the Int. Conf. on Systems, Man, and Cybernetics, Vol.2, pp. 1556-1561, 1995.
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