Natural Evolution Strategies
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Tom Schaul | Jan Peters | Daan Wierstra | Juergen Schmidhuber | T. Schaul | Jan Peters | Daan Wierstra | J. Schmidhuber
[1] R. A. Leibler,et al. On Information and Sufficiency , 1951 .
[2] John A. Nelder,et al. A Simplex Method for Function Minimization , 1965, Comput. J..
[3] Hans-Paul Schwefel,et al. TWO-PHASE NOZZLE AND HOLLOW CORE JET EXPERIMENTS. , 1970 .
[4] Ingo Rechenberg,et al. Evolutionsstrategie : Optimierung technischer Systeme nach Prinzipien der biologischen Evolution , 1973 .
[5] W. Vent,et al. Rechenberg, Ingo, Evolutionsstrategie — Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. 170 S. mit 36 Abb. Frommann‐Holzboog‐Verlag. Stuttgart 1973. Broschiert , 1975 .
[6] John H. Holland,et al. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .
[7] H. P. Schwefel,et al. Numerische Optimierung von Computermodellen mittels der Evo-lutionsstrategie , 1977 .
[8] C. D. Gelatt,et al. Optimization by Simulated Annealing , 1983, Science.
[9] David E. Goldberg,et al. Genetic Algorithms in Search Optimization and Machine Learning , 1988 .
[10] A. P. Wieland,et al. Evolving neural network controllers for unstable systems , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.
[11] John R. Koza,et al. Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.
[12] Martin A. Riedmiller,et al. A direct adaptive method for faster backpropagation learning: the RPROP algorithm , 1993, IEEE International Conference on Neural Networks.
[13] Nikolaus Hansen,et al. Step-Size Adaption Based on Non-Local Use of Selection Information , 1994, PPSN.
[14] Timothy F. Havel,et al. Derivatives of the Matrix Exponential and Their Computation , 1995 .
[15] Hans-Georg Beyer,et al. Toward a Theory of Evolution Strategies: Self-Adaptation , 1995, Evolutionary Computation.
[16] H. Mühlenbein,et al. From Recombination of Genes to the Estimation of Distributions I. Binary Parameters , 1996, PPSN.
[17] J. Doye,et al. Global Optimization by Basin-Hopping and the Lowest Energy Structures of Lennard-Jones Clusters Containing up to 110 Atoms , 1997, cond-mat/9803344.
[18] Rafal Salustowicz,et al. Probabilistic Incremental Program Evolution , 1997, Evolutionary Computation.
[19] Rainer Storn,et al. Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..
[20] Takuji Nishimura,et al. Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator , 1998, TOMC.
[21] Shun-ichi Amari,et al. Natural Gradient Works Efficiently in Learning , 1998, Neural Computation.
[22] Shun-ichi Amari,et al. Why natural gradient? , 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).
[23] James C. Spall,et al. Stochastic optimization and the simultaneous perturbation method , 1999, WSC '99.
[24] Risto Miikkulainen,et al. Solving Non-Markovian Control Tasks with Neuro-Evolution , 1999, IJCAI.
[25] Arnaud Berny. Selection and Reinforcement Learning for Combinatorial Optimization , 2000, PPSN.
[26] Dirk Thierens,et al. Expanding from Discrete to Continuous Estimation of Distribution Algorithms: The IDEA , 2000, PPSN.
[27] J. A. Lozano,et al. Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation , 2001 .
[28] Hans-Georg Beyer,et al. The Theory of Evolution Strategies , 2001, Natural Computing Series.
[29] Sham M. Kakade,et al. A Natural Policy Gradient , 2001, NIPS.
[30] A. Berny,et al. Statistical machine learning and combinatorial optimization , 2001 .
[31] Nikolaus Hansen,et al. Completely Derandomized Self-Adaptation in Evolution Strategies , 2001, Evolutionary Computation.
[32] J. Spall,et al. Theoretical framework for comparing several popular stochastic optimization approaches , 2002 .
[33] David E. Goldberg,et al. A Survey of Optimization by Building and Using Probabilistic Models , 2002, Comput. Optim. Appl..
[34] Petros Koumoutsakos,et al. Optimization based on bacterial chemotaxis , 2002, IEEE Trans. Evol. Comput..
[35] Jeff G. Schneider,et al. Covariant Policy Search , 2003, IJCAI.
[36] Jens Jägersküpper,et al. Analysis of a Simple Evolutionary Algorithm for Minimization in Euclidean Spaces , 2003, ICALP.
[37] Christian Igel,et al. Empirical evaluation of the improved Rprop learning algorithms , 2003, Neurocomputing.
[38] J. Spall,et al. Theoretical framework for comparing several popular stochastic optimization approaches , 2002 .
[39] Peter Stone,et al. Policy gradient reinforcement learning for fast quadrupedal locomotion , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.
[40] Ronald J. Williams,et al. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning , 2004, Machine Learning.
[41] Peter A. N. Bosman,et al. Learning Probabilistic Tree Grammars for Genetic Programming , 2004, PPSN.
[42] Christian Igel,et al. Evolutionary tuning of multiple SVM parameters , 2005, ESANN.
[43] Hans-Paul Schwefel,et al. Evolution strategies – A comprehensive introduction , 2002, Natural Computing.
[44] Dirk P. Kroese,et al. The Cross Entropy Method: A Unified Approach To Combinatorial Optimization, Monte-carlo Simulation (Information Science and Statistics) , 2004 .
[45] Christian Igel,et al. Registration of bone structures in 3D ultrasound and CT data: Comparison of different optimization strategies , 2005 .
[46] Christian Igel,et al. Gradient-Based Adaptation of General Gaussian Kernels , 2005, Neural Computation.
[47] Bernhard Sendhoff,et al. Three dimensional evolutionary aerodynamic design optimization with CMA-ES , 2005, GECCO '05.
[48] Anne Auger,et al. Convergence results for the (1, lambda)-SA-ES using the theory of phi-irreducible Markov chains , 2005, Theor. Comput. Sci..
[49] Jing J. Liang,et al. Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization , 2005 .
[50] A. Auger. Convergence results for the ( 1 , )-SA-ES using the theory of-irreducible Markov chains , 2005 .
[51] Stefan Schaal,et al. Natural Actor-Critic , 2003, Neurocomputing.
[52] Dirk V. Arnold,et al. Improving Evolution Strategies through Active Covariance Matrix Adaptation , 2006, 2006 IEEE International Conference on Evolutionary Computation.
[53] Geoffrey E. Hinton,et al. Reducing the Dimensionality of Data with Neural Networks , 2006, Science.
[54] Lih-Yuan Deng,et al. The Cross-Entropy Method: A Unified Approach to Combinatorial Optimization, Monte-Carlo Simulation, and Machine Learning , 2006, Technometrics.
[55] Stefan Schaal,et al. Policy Gradient Methods for Robotics , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.
[56] Martin Pelikan,et al. Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications (Studies in Computational Intelligence) , 2006 .
[57] J. Shepherd,et al. Modeling morphology evolution and mechanical behavior during thermo-mechanical processing of semi-crystalline polymers , 2006 .
[58] Jens Jägersküpper,et al. Algorithmic analysis of a basic evolutionary algorithm for continuous optimization , 2007, Theor. Comput. Sci..
[59] P. Bosman,et al. Adapted Maximum-Likelihood Gaussian Models for Numerical Optimization with Continuous EDAs , 2007 .
[60] Ofer M. Shir,et al. The second harmonic generation case-study as a gateway for es to quantum control problems , 2007, GECCO '07.
[61] Mauro Birattari,et al. Swarm Intelligence , 2012, Lecture Notes in Computer Science.
[62] Anne Auger,et al. Identification of the isotherm function in chromatography using CMA-ES , 2007, 2007 IEEE Congress on Evolutionary Computation.
[63] Raymond Ros,et al. A Simple Modification in CMA-ES Achieving Linear Time and Space Complexity , 2008, PPSN.
[64] Risto Miikkulainen,et al. Accelerated Neural Evolution through Cooperatively Coevolved Synapses , 2008, J. Mach. Learn. Res..
[65] Christian Igel,et al. Similarities and differences between policy gradient methods and evolution strategies , 2008, ESANN.
[66] Jan Peters,et al. Machine Learning for motor skills in robotics , 2008, Künstliche Intell..
[67] Tom Schaul,et al. Efficient natural evolution strategies , 2009, GECCO.
[68] Petros Koumoutsakos,et al. A Method for Handling Uncertainty in Evolutionary Optimization With an Application to Feedback Control of Combustion , 2009, IEEE Transactions on Evolutionary Computation.
[69] Nikolaus Hansen,et al. Benchmarking a BI-population CMA-ES on the BBOB-2009 noisy testbed , 2009, GECCO '09.
[70] Jürgen Schmidhuber,et al. Simple algorithmic theory of subjective beauty, novelty, surprise, interestingness, attention, curiosity, creativity, art, science, music, jokes (特集 高次機能の学習と創発--脳・ロボット・人間研究における新たな展開) , 2009 .
[71] Geoffrey E. Hinton,et al. Deep Boltzmann Machines , 2009, AISTATS.
[72] Raymond Ros,et al. Real-Parameter Black-Box Optimization Benchmarking 2009: Experimental Setup , 2009 .
[73] Anne Auger,et al. Real-Parameter Black-Box Optimization Benchmarking 2009: Noiseless Functions Definitions , 2009 .
[74] Tom Schaul,et al. Stochastic search using the natural gradient , 2009, ICML '09.
[75] Nikolaus Hansen,et al. Benchmarking a BI-population CMA-ES on the BBOB-2009 function testbed , 2009, GECCO '09.
[76] Anne Auger,et al. Log-Linear Convergence and Divergence of the Scale-Invariant (1+1)-ES in Noisy Environments , 2011, Algorithmica.
[77] Isao Ono,et al. Bidirectional Relation between CMA Evolution Strategies and Natural Evolution Strategies , 2010, PPSN.
[78] Tom Schaul,et al. Exponential natural evolution strategies , 2010, GECCO '10.
[79] Tom Schaul,et al. A Natural Evolution Strategy for Multi-objective Optimization , 2010, PPSN.
[80] Tom Schaul,et al. Towards Practical Universal Search , 2010, AGI 2010.