Solving the MAXSAT problem using a multivariate EDA based on Markov networks

Markov Networks (also known as Markov Random Fields) have been proposed as a new approach to probabilistic modelling in Estimation of Distribution Algorithms (EDAs). An EDA employing this approach called Distribution Estimation Using Markov Networks (DEUM) has been proposed and shown to work well on a variety of problems, using a unique fitness modelling approach. Previously DEUM has only been demonstrated on univariate and bivariate complexity problems. Here we show that it can be extended to a difficult multivariate problem and is capable of accurately modelling a fitness function and locating an optimum with a very small number of function evaluations. Categories and Subject Descriptors I.2.8 [Artificial Intelligence]: Problem Solving, Control Methods, and Search; G.3 [Probability and statistics]: Probabilistic algorithms, Stochastic processes General Terms Algorithms, Performance, Theory Keywords Estimation of Distribution Algorithms, Evolutionary Algorithms, Probabilistic Modelli...

• Type: Conference
• Year: 2007
• Where: GECCO
• Authors: Alexander E. I. Brownlee, John A. W. McCall, Deryck F. Brown
• Keywords: Distribution Algorithms, GECCO 2007, Markov Networks, Probabilistic Modelling