Optimisation and Fitness Modelling of Bio-control in Mushroom Farming Using a Markov Network EDA

Citation

Brownlee A, Wu Y, McCall J, Godley PM, Cairns D & Cowie J (2008) Optimisation and Fitness Modelling of Bio-control in Mushroom Farming Using a Markov Network EDA. In: Keijzer M (ed.) Proceedings of the 10th annual conference on Genetic and evolutionary computation, (GECCO-2008). Genetic and Evolutionary Computation Conference, GECCO-2008, Atlanta, Georgia, 12.07.2008-16.07.2008. New York: Association for Computing Machinery (ACM), pp. 465-466. https://doi.org/10.1145/1389095.1389180

Abstract
We explore the application of an Estimation of Distribution Algorithm which uses a Markov Network to the problem of bio-control in mushroom farming. This falls into the category of “bang-bang control” problems and was previously used as an application for genetic algorithms with modified crossover operators. The EDA yields a small improvement in the solutions that are evolved. Moreover, the probabilistic models constructed closely match identifiable features in the underlying dynamics of the problem. We conclude that this is a useful by-product of the probabilistic modelling which can be further exploited. probabilistic model learned by DEUM. We will describe how a clear relationship can be drawn between probabilistic model and the underlying shape of the problem.

Keywords
Optimisation; EDA; Optimal Control; Genetics Mathematical models; Genetics Computer simulation; Control theory