### Recombination operators and selection strategies for evolutionary Markov Chain Monte Carlo algorithms

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[1] W. K. Hastings,et al. Monte Carlo Sampling Methods Using Markov Chains and Their Applications , 1970 .

[2] David E. Goldberg,et al. A Survey of Optimization by Building and Using Probabilistic Models , 2002, Comput. Optim. Appl..

[3] Cajo J. F. ter Braak,et al. A Markov Chain Monte Carlo version of the genetic algorithm Differential Evolution: easy Bayesian computing for real parameter spaces , 2006, Stat. Comput..

[4] Nando de Freitas,et al. An Introduction to MCMC for Machine Learning , 2004, Machine Learning.

[5] C.J.F. ter Braak,et al. Genetic algorithms and Markov Chain Monte Carlo: Differential Evolution Markov Chain makes Bayesian computing easy (revised) , 2004 .

[6] C. Braak,et al. Genetic algorithms and Markov Chain Monte Carlo: Differential Evolution Markov Chain makes Bayesian computing easy , 2004 .

[7] Nicholas J. Radcliffe,et al. Forma Analysis and Random Respectful Recombination , 1991, ICGA.

[8] Jukka Corander,et al. Parallell interacting MCMC for learning of topologies of graphical models , 2008, Data Mining and Knowledge Discovery.

[9] A. Gelman,et al. Weak convergence and optimal scaling of random walk Metropolis algorithms , 1997 .

[10] Peter Green,et al. Markov chain Monte Carlo in Practice , 1996 .

[11] S. Q. s3idChMn,et al. Evolutionary Monte Carlo: Applications to C_p Model Sampling and Change Point Problem , 2000 .

[12] C. N. Liu,et al. Approximating discrete probability distributions with dependence trees , 1968, IEEE Trans. Inf. Theory.

[13] C. D. Gelatt,et al. Optimization by Simulated Annealing , 1983, Science.

[14] Kathryn B. Laskey,et al. Population Markov Chain Monte Carlo , 2004, Machine Learning.

[15] N. Metropolis,et al. Equation of State Calculations by Fast Computing Machines , 1953, Resonance.

[16] Malcolm J. A. Strens,et al. Evolutionary MCMC Sampling and Optimization in Discrete Spaces , 2003, ICML.

[17] Byoung-Tak Zhang,et al. System identification using evolutionary Markov chain Monte Carlo , 2001, J. Syst. Archit..

[18] Joseph Geraci,et al. A new connection between quantum circuits, graphs and the Ising partition function , 2008, Quantum Inf. Process..

[19] Fabien Campillo,et al. Parallel and interacting Markov chain Monte Carlo algorithm , 2009, Math. Comput. Simul..

[20] Timothy J. Robinson,et al. Sequential Monte Carlo Methods in Practice , 2003 .

[21] Shumeet Baluja,et al. Using Optimal Dependency-Trees for Combinational Optimization , 1997, ICML.

[22] Dirk Thierens,et al. Elitist recombination: an integrated selection recombination GA , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[23] Paul Fearnhead. Editorial: Special issue on adaptive Monte Carlo methods , 2008, Stat. Comput..

[24] Rainer Storn,et al. Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[25] D. Wolpert,et al. Adaptive Metropolis Sampling and Optimization with Product Distributions , 2005 .

[26] David E. Goldberg,et al. Parallel Recombinative Simulated Annealing: A Genetic Algorithm , 1995, Parallel Comput..

[27] Fernando G. Lobo,et al. A Survey of Optimization by Building and Using Probabilistic Models , 2000, Proceedings of the 2000 American Control Conference. ACC (IEEE Cat. No.00CH36334).

[28] Dirk Thierens,et al. Evolutionary Markov Chain Monte Carlo , 2003, Artificial Evolution.

[29] C. Geyer. Markov Chain Monte Carlo Maximum Likelihood , 1991 .

[30] Faming Liang,et al. EVOLUTIONARY MONTE CARLO: APPLICATIONS TO Cp MODEL SAMPLING AND CHANGE POINT PROBLEM , 2000 .

[31] Dirk Thierens,et al. Recombinative EMCMC algorithms , 2005, 2005 IEEE Congress on Evolutionary Computation.