Parameter-less Hierarchical Bayesian Optimization Algorithm

[1]  Martin Pelikan,et al.  Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications (Studies in Computational Intelligence) , 2006 .

[2]  Martin Pelikan,et al.  Searching for Ground States of Ising Spin Glasses with Hierarchical BOA and Cluster Exact Approximation , 2006, Scalable Optimization via Probabilistic Modeling.

[3]  F. Guerra,et al.  Spin Glasses , 2005, cond-mat/0507581.

[4]  Kalyanmoy Deb,et al.  Sufficient conditions for deceptive and easy binary functions , 1994, Annals of Mathematics and Artificial Intelligence.

[5]  Cláudio F. Lima,et al.  Parameter-Less Optimization with the Extended Compact Genetic Algorithm and Iterated Local Search , 2004, GECCO.

[6]  Martin Pelikan,et al.  Parameter-Less Hierarchical BOA , 2004, GECCO.

[7]  Martin Pelikan,et al.  Hierarchical Bayesian optimization algorithm: toward a new generation of evolutionary algorithms , 2010, SICE 2003 Annual Conference (IEEE Cat. No.03TH8734).

[8]  David E. Goldberg,et al.  Hierarchical BOA Solves Ising Spin Glasses and MAXSAT , 2003, GECCO.

[9]  David E. Goldberg,et al.  A hierarchy machine: Learning to optimize from nature and humans , 2003, Complex..

[10]  David E. Goldberg,et al.  Scalability of the Bayesian optimization algorithm , 2002, Int. J. Approx. Reason..

[11]  A. Middleton,et al.  Three-dimensional random-field Ising magnet: Interfaces, scaling, and the nature of states , 2001, cond-mat/0107489.

[12]  David E. Goldberg,et al.  Bayesian Optimization Algorithm: From Single Level to Hierarchy , 2002 .

[13]  J. A. Lozano,et al.  Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation , 2001 .

[14]  D. Goldberg,et al.  Escaping hierarchical traps with competent genetic algorithms , 2001 .

[15]  Martin Pelikan,et al.  Parameter-less Genetic Algorithm: A Worst-case Time and Space Complexity Analysis , 2000, GECCO.

[16]  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).

[17]  P. Bosman,et al.  Continuous iterated density estimation evolutionary algorithms within the IDEA framework , 2000 .

[18]  Fernando G. Lobo,et al.  A parameter-less genetic algorithm , 1999, GECCO.

[19]  D. Goldberg,et al.  Domino convergence, drift, and the temporal-salience structure of problems , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[20]  David E. Goldberg,et al.  The compact genetic algorithm , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[21]  David Maxwell Chickering,et al.  A Bayesian Approach to Learning Bayesian Networks with Local Structure , 1997, UAI.

[22]  H. Mühlenbein,et al.  From Recombination of Genes to the Estimation of Distributions I. Binary Parameters , 1996, PPSN.

[23]  Georges R. Harik,et al.  Finding Multimodal Solutions Using Restricted Tournament Selection , 1995, ICGA.

[24]  Dirk Thierens,et al.  Convergence Models of Genetic Algorithm Selection Schemes , 1994, PPSN.

[25]  Heinz Mühlenbein,et al.  Predictive Models for the Breeder Genetic Algorithm I. Continuous Parameter Optimization , 1993, Evolutionary Computation.

[26]  Kalyanmoy Deb,et al.  Analyzing Deception in Trap Functions , 1992, FOGA.

[27]  Heinz Mühlenbein,et al.  How Genetic Algorithms Really Work: Mutation and Hillclimbing , 1992, PPSN.

[28]  M. Mézard,et al.  Spin Glass Theory and Beyond , 1987 .

[29]  David H. Ackley,et al.  An empirical study of bit vector function optimization , 1987 .

[30]  K. Binder,et al.  Spin glasses: Experimental facts, theoretical concepts, and open questions , 1986 .