A new evolutionary search strategy for global optimization of high-dimensional problems
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[1] Riccardo Poli,et al. Evolving Problems to Learn About Particle Swarm Optimizers and Other Search Algorithms , 2006, IEEE Transactions on Evolutionary Computation.
[2] Shiu Yin Yuen,et al. A Genetic Algorithm That Adaptively Mutates and Never Revisits , 2009, IEEE Trans. Evol. Comput..
[3] R. Bellman. Dynamic programming. , 1957, Science.
[4] Bruce A. Robinson,et al. Self-Adaptive Multimethod Search for Global Optimization in Real-Parameter Spaces , 2009, IEEE Transactions on Evolutionary Computation.
[5] Wei Chu,et al. A Solution to the Crucial Problem of Population Degeneration in High-Dimensional Evolutionary Optimization , 2011, IEEE Systems Journal.
[6] Bin Li,et al. Multi-strategy ensemble particle swarm optimization for dynamic optimization , 2008, Inf. Sci..
[7] Toshio Odanaka,et al. ADAPTIVE CONTROL PROCESSES , 1990 .
[8] Nikolaus Hansen,et al. On the Adaptation of Arbitrary Normal Mutation Distributions in Evolution Strategies: The Generating Set Adaptation , 1995, ICGA.
[9] S. Sorooshian,et al. Effective and efficient global optimization for conceptual rainfall‐runoff models , 1992 .
[10] Jeffrey C. Lagarias,et al. Convergence Properties of the Nelder-Mead Simplex Method in Low Dimensions , 1998, SIAM J. Optim..
[11] Jing J. Liang,et al. Dynamic multi-swarm particle swarm optimizer with local search for Large Scale Global Optimization , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).
[12] David H. Wolpert,et al. No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..
[13] Amit Konar,et al. Differential Evolution Using a Neighborhood-Based Mutation Operator , 2009, IEEE Transactions on Evolutionary Computation.
[14] George Kuczera,et al. Probabilistic optimization for conceptual rainfall-runoff models: A comparison of the shuffled complex evolution and simulated annealing algorithms , 1999 .
[15] C. T. Kelley,et al. Detection and Remediation of Stagnation in the Nelder--Mead Algorithm Using a Sufficient Decrease Condition , 1999, SIAM J. Optim..
[16] Andries Petrus Engelbrecht,et al. A Cooperative approach to particle swarm optimization , 2004, IEEE Transactions on Evolutionary Computation.
[17] Rainer Storn,et al. Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..
[18] Josien P. W. Pluim,et al. Evaluation of Optimization Methods for Nonrigid Medical Image Registration Using Mutual Information and B-Splines , 2007, IEEE Transactions on Image Processing.
[19] Nikolaus Hansen,et al. Adapting arbitrary normal mutation distributions in evolution strategies: the covariance matrix adaptation , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.
[20] Sanghamitra Bandyopadhyay,et al. Multi-Objective Particle Swarm Optimization with time variant inertia and acceleration coefficients , 2007, Inf. Sci..
[21] Jing J. Liang,et al. Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization , 2005 .
[22] Janez Brest,et al. Self-Adapting Control Parameters in Differential Evolution: A Comparative Study on Numerical Benchmark Problems , 2006, IEEE Transactions on Evolutionary Computation.
[23] Wei Chu,et al. Handling boundary constraints for particle swarm optimization in high-dimensional search space , 2011, Inf. Sci..
[24] Peng Shi,et al. Using investment satisfaction capability index based particle swarm optimization to construct a stock portfolio , 2011, Inf. Sci..
[25] Hitoshi Iba,et al. Accelerating Differential Evolution Using an Adaptive Local Search , 2008, IEEE Transactions on Evolutionary Computation.
[26] Jing J. Liang,et al. Novel composition test functions for numerical global optimization , 2005, Proceedings 2005 IEEE Swarm Intelligence Symposium, 2005. SIS 2005..
[27] C. D. Gelatt,et al. Optimization by Simulated Annealing , 1983, Science.
[28] José Neves,et al. The fully informed particle swarm: simpler, maybe better , 2004, IEEE Transactions on Evolutionary Computation.
[29] M.M.A. Salama,et al. Opposition-Based Differential Evolution , 2008, IEEE Transactions on Evolutionary Computation.
[30] S. Sorooshian,et al. Shuffled complex evolution approach for effective and efficient global minimization , 1993 .
[31] K. I. M. McKinnon,et al. Convergence of the Nelder-Mead Simplex Method to a Nonstationary Point , 1998, SIAM J. Optim..
[32] Soroosh Sorooshian,et al. Calibration of rainfall‐runoff models: Application of global optimization to the Sacramento Soil Moisture Accounting Model , 1993 .
[33] R. Bellman,et al. V. Adaptive Control Processes , 1964 .
[34] Wenjian Luo,et al. Differential evolution with dynamic stochastic selection for constrained optimization , 2008, Inf. Sci..
[35] Adam Liwo,et al. Recent improvements in prediction of protein structure by global optimization of a potential energy function , 2001, Proceedings of the National Academy of Sciences of the United States of America.