Can Single Solution Optimisation Methods Be Structurally Biased?
暂无分享,去创建一个
[1] Xinchao Zhao,et al. Simulated annealing algorithm with adaptive neighborhood , 2011, Appl. Soft Comput..
[2] Christian Igel,et al. A computational efficient covariance matrix update and a (1+1)-CMA for evolution strategies , 2006, GECCO.
[3] Robert Hooke,et al. `` Direct Search'' Solution of Numerical and Statistical Problems , 1961, JACM.
[4] J. Shaffer. Multiple Hypothesis Testing , 1995 .
[5] M. J. D. Powell,et al. An efficient method for finding the minimum of a function of several variables without calculating derivatives , 1964, Comput. J..
[6] Edmund K. Burke,et al. A Separability Prototype for Automatic Memes with Adaptive Operator Selection , 2014, 2014 IEEE Symposium on Foundations of Computational Intelligence (FOCI).
[7] David H. Wolpert,et al. No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..
[8] Roger J.-B. Wets,et al. Minimization by Random Search Techniques , 1981, Math. Oper. Res..
[9] Fabio Caraffini,et al. Structural Bias in Optimisation Algorithms: Extended Results , 2020 .
[10] Adam P. Piotrowski,et al. Some metaheuristics should be simplified , 2018, Inf. Sci..
[11] Ji Zhen. A Novel Intelligent Single Particle Optimizer , 2010 .
[12] Fabio Caraffini. The SOS Platform: Designing, Tuning and Statistically Benchmarking Optimisation Algorithms , 2020, Mathematics.
[13] Ralph B. D'Agostino,et al. Goodness-of-Fit-Techniques , 2020 .
[14] David W. Corne,et al. Structural bias in population-based algorithms , 2014, Inf. Sci..
[15] D. Anderson,et al. Algorithms for minimization without derivatives , 1974 .
[16] Adam P. Piotrowski,et al. Across Neighborhood Search algorithm: A comprehensive analysis , 2018, Inf. Sci..
[17] J. R. Palmer. An Improved Procedure for Orthogonalising the Search Vectors in Rosenbrock's and Swann's Direct Search Optimisation Methods , 1969, Comput. J..
[18] Adam Prügel-Bennett,et al. Benefits of a Population: Five Mechanisms That Advantage Population-Based Algorithms , 2010, IEEE Transactions on Evolutionary Computation.
[19] C. D. Gelatt,et al. Optimization by Simulated Annealing , 1983, Science.
[20] J. Spall. A Stochastic Approximation Technique for Generating Maximum Likelihood Parameter Estimates , 1987, 1987 American Control Conference.
[21] Giovanni Iacca,et al. Single particle algorithms for continuous optimization , 2013, 2013 IEEE Congress on Evolutionary Computation.
[22] Giovanni Iacca,et al. Re-sampled inheritance search: high performance despite the simplicity , 2013, Soft Comput..
[23] Günter Rudolph,et al. Contemporary Evolution Strategies , 1995, ECAL.
[24] David H. Wolpert,et al. Coevolutionary free lunches , 2005, IEEE Transactions on Evolutionary Computation.
[25] Giovanni Iacca,et al. Improving (1+1) covariance matrix adaptation evolution strategy: a simple yet efficient approach , 2019 .
[26] Anne Auger,et al. Benchmarking the (1+1) evolution strategy with one-fifth success rule on the BBOB-2009 function testbed , 2009, GECCO '09.
[27] Adam P. Piotrowski,et al. Searching for structural bias in particle swarm optimization and differential evolution algorithms , 2016, Swarm Intelligence.
[28] Francisco Herrera,et al. MA-SW-Chains: Memetic algorithm based on local search chains for large scale continuous global optimization , 2010, IEEE Congress on Evolutionary Computation.
[29] Adam P. Piotrowski,et al. Step-by-step improvement of JADE and SHADE-based algorithms: Success or failure? , 2018, Swarm Evol. Comput..
[30] Fabio Caraffini,et al. Structural bias in differential evolution: A preliminary study , 2019 .
[31] Ferrante Neri,et al. Differential Evolution with Scale Factor Local Search for Large Scale Problems , 2010 .
[32] John A. Nelder,et al. A Simplex Method for Function Minimization , 1965, Comput. J..
[33] Y. Benjamini. Discovering the false discovery rate , 2010 .
[34] J. Spall. Implementation of the simultaneous perturbation algorithm for stochastic optimization , 1998 .
[35] David W. Corne,et al. Infeasibility and structural bias in Differential Evolution , 2019, Inf. Sci..
[36] Alden H. Wright,et al. Emergent Behaviour, Population-based Search and Low-pass Filtering , 2006, 2006 IEEE International Conference on Evolutionary Computation.
[37] H. H. Rosenbrock,et al. An Automatic Method for Finding the Greatest or Least Value of a Function , 1960, Comput. J..
[38] Alfred Inselberg,et al. The plane with parallel coordinates , 1985, The Visual Computer.
[39] Warren Hare,et al. Best practices for comparing optimization algorithms , 2017, Optimization and Engineering.
[40] Michael G. Epitropakis,et al. HyperSPAM: A study on hyper-heuristic coordination strategies in the continuous domain , 2019, Inf. Sci..
[41] Ingo Rechenberg,et al. Evolutionsstrategie : Optimierung technischer Systeme nach Prinzipien der biologischen Evolution , 1973 .
[42] Hans-Paul Schwefel,et al. Evolution and optimum seeking , 1995, Sixth-generation computer technology series.
[43] Riccardo Poli,et al. Particle swarm optimization , 1995, Swarm Intelligence.
[44] Fabio Caraffini,et al. The Stochastic Optimisation Software (SOS) platform , 2019 .