Paper Info
Title
Bio-control in mushroom farming using a Markov network EDA
Abstract
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.
Year
DOI
Venue
2008
10.1109/CEC.2008.4631201
IEEE Congress on Evolutionary Computation
Keywords
Field
DocType
bang-bang-control solutions,biocontrol,evolutionary computation,markov network eda,distribution algorithm estimation,weighted fitness function,mushroom farming,markov processes,estimation of distribution algorithm,fitness function,gallium,evolutionary algorithms,tin,ions,eda,genetic algorithm,algorithm design,bang bang control,probabilistic model
Mathematical optimization,Crossover,Markov process,Evolutionary algorithm,Estimation of distribution algorithm,Computer science,Markov model,Markov chain,Fitness function,Artificial intelligence,Machine learning,Genetic algorithm
Conference
ISBN
Citations 
PageRank 
978-1-4244-1823-7
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Yanghui Wu1282.36
John McCall214817.36
Paul M. Godley381.71
Alexander E.I. Brownlee414418.46
David E. Cairns5355.23
Julie Cowie6415.46