Markovian Learning Estimation of Distribution Algorithm (MARLEDA) is
an Estimation of Distribution Algorithm (EDA) that employs a Markov
Random Field model. EDAs in general combine genetic algorithms with
statistical modeling in order to learn and exploit the structure of
search domains. Most EDAs use directed acyclic graphs (DAGs) as
models; while they are useful in many areas, DAGs have inherent
restrictions that make undirected graph models a viable alternative in
some domains. Markov Random Fields allows constructing such an
undirected model. Example tasks (in this package) include OneMax,
deceptive trap functions, the 2D Rosenbrock function, and 2D Ising
spin glasses. Potential applications include design of autonomous
agents as well as optimization in computational biology, such as
RNA structure prediction (described e.g. in
Alden's PhD thesis ).
Also show archived content
Neuroevolution  Risto Miikkulainen  To Appear In Dinh Phung, Claude Sammut and Geoffrey I. Webb, editors, Encyclopedia of Machine Lea...  2021 

MARLEDA: Effective Distribution Estimation through Markov Random Fields  Matthew Alden and Risto Miikkulainen  Theoretical Computer Science, 633:418, 2016.  2016 

Neuroevolution  Risto Miikkulainen  In Sammut, C. and Webb, G. I., editors, Encyclopedia of Machine Learning, 2nd Edition, Berlin...  2015 

MARLEDA: Effective Distribution Estimation Through Markov Random Fields  Matthew Alden  PhD Thesis, Department of Computer Sciences, the University of Texas at Austin, Austin, Texas, 2007....  2007 
