Simulated annealing and Boltzmann machines: a stochastic approach to combinatorial optimization and neural computing
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Markov Random Field Modelling of Royal Road Genetic Algorithms
Selected Papers from the 5th European Conference on Artificial Evolution
Image Analysis, Random Fields and Markov Chain Monte Carlo Methods: A Mathematical Introduction (Stochastic Modelling and Applied Probability)
Estimation of Sparse Binary Pairwise Markov Networks using Pseudo-likelihoods
The Journal of Machine Learning Research
A fully multivariate DEUM algorithm
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Effective structure learning for EDA via L1-regularizedbayesian networks
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Towards the geometry of estimation of distribution algorithms based on the exponential family
Proceedings of the 11th workshop proceedings on Foundations of genetic algorithms
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When the function to be optimized is characterized by a limited and unknown number of interactions among variables, a context that applies to many real world scenario, it is possible to design optimization algorithms based on such information. Estimation of Distribution Algorithms learn a set of interactions from a sample of points and encode them in a probabilistic model. The latter is then used to sample new instances. In this paper, we propose a novel approach to estimate the Markov Fitness Model used in DEUM. We combine model selection and model fitting by solving an ℓ1-constrained linear regression problem. Since candidate interactions grow exponentially in the size of the problem, we first reduce this set with a preliminary coarse selection criteria based on Mutual Information. Then, we employ ℓ1-regularization to further enforce sparsity in the model, estimating its parameters at the same time. Our proposal is analyzed against the 3D Ising Spin Glass function, a problem known to be NP-hard, and it outperforms other popular black-box meta-heuristics.