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. Categories and Subject Descriptor
In 1-year experiments, the final population density of nematodes is usually modeled as a function of...
We study the problem of finding optimal control policies for Probabilistic Boolean Networks (PBNs)....
Control of Probabilistic Boolean Networks as models of gene regulation is an important problem; the ...
We explore the application of an Estimation of Distribution Algorithm which uses a Markov Network to...
We explore the application of an Estimation of Distribution Algorithm which uses a Markov Network to...
In this paper we present an application of an Estimation of Distribution Algorithm (EDA) that uses a...
In recent years, Markov Network EDAs have begun to find application to a range of important scientif...
Markov networks and other probabilistic graphical modes have recently received an upsurge in attenti...
Even with recent improvements, the agricultural production is still highly affected by pathogens. In...
Decision support systems are gaining importance in several fields of agriculture, forest, and ecolog...
The construction and control of genetic regulatory networks using gene expression data is an importa...
International audienceAugmentative biological control is a crop protection method that relies on the...
A well-known paradigm for optimisation is the evolutionary algorithm (EA). An EA maintains a populat...
This paper addresses the control formulation process for probabilistic boolean genetic networks. It ...
To solve some function optimization problems, the PDO-MCCE algorithm is proposed by using the popula...
In 1-year experiments, the final population density of nematodes is usually modeled as a function of...
We study the problem of finding optimal control policies for Probabilistic Boolean Networks (PBNs)....
Control of Probabilistic Boolean Networks as models of gene regulation is an important problem; the ...
We explore the application of an Estimation of Distribution Algorithm which uses a Markov Network to...
We explore the application of an Estimation of Distribution Algorithm which uses a Markov Network to...
In this paper we present an application of an Estimation of Distribution Algorithm (EDA) that uses a...
In recent years, Markov Network EDAs have begun to find application to a range of important scientif...
Markov networks and other probabilistic graphical modes have recently received an upsurge in attenti...
Even with recent improvements, the agricultural production is still highly affected by pathogens. In...
Decision support systems are gaining importance in several fields of agriculture, forest, and ecolog...
The construction and control of genetic regulatory networks using gene expression data is an importa...
International audienceAugmentative biological control is a crop protection method that relies on the...
A well-known paradigm for optimisation is the evolutionary algorithm (EA). An EA maintains a populat...
This paper addresses the control formulation process for probabilistic boolean genetic networks. It ...
To solve some function optimization problems, the PDO-MCCE algorithm is proposed by using the popula...
In 1-year experiments, the final population density of nematodes is usually modeled as a function of...
We study the problem of finding optimal control policies for Probabilistic Boolean Networks (PBNs)....
Control of Probabilistic Boolean Networks as models of gene regulation is an important problem; the ...