Bio-control in mushroom farming using a Markov network EDA

In this paper we present an application of an Estimation of Distribution Algorithm (EDA) that uses a Markov network probabilistic model. The application is to the problem of bio-control in mushroom farming, a domain which admits bang-bang-control solutions. The problem is multi-objective and uses a weighted fitness function. Previous work on this problem has applied genetic algorithms (GA) with directed intervention crossover schemes aimed at effective biocontrol at an efficient level of intervention. Here we compare these approaches with the EDA Distribution Estimation Using Markov networks (DEUMd). DEUMd constructs a probabilistic model using Markov networks. Our experiments compare the quality of solutions produced by DEUMd with the GA approaches and also reveal interesting differences in the search dynamics that have implications for algorithm design.