Solving the MAXSAT problem using a multivariate EDA based on Markov networks
Alexander E. I. Brownlee, John McCall, Deryck F. Brown · 2007
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.