- author = "Martin Pelikan",
- title = "Probabilistic model-building genetic algorithms",
- booktitle = "GECCO 2011 Late breaking abstracts",
- year = "2011",
- editor = "Christian Blum",
- isbn13 = "978-1-4503-0690-4",
- keywords = "genetic algorithms, genetic programming",
- pages = "913--940",
- month = "12-16 " # jul,
- organisation = "SIGEVO",
- address = "Dublin, Ireland",
-
DOI = "
doi:10.1145/2001858.2002120",
- publisher = "ACM",
- publisher_address = "New York, NY, USA",
-
abstract = "Probabilistic model-building algorithms (PMBGAs)
replace traditional variation of genetic and
evolutionary algorithms by (1) building a probabilistic
model of promising solutions and (2) sampling the built
model to generate new candidate solutions. PMBGAs are
also known as estimation of distribution algorithms
(EDAs) and iterated density-estimation algorithms
(IDEAs).
Replacing traditional crossover and mutation operators by building and sampling a probabilistic model of promising solutions enables the use of machine learning techniques for automatic discovery of problem regularities and exploitation of these regularities for effective exploration of the search space. Using machine learning in optimisation enables the design of optimisation techniques that can automatically adapt to the given problem. There are many successful applications of PMBGAs, for example, Ising spin glasses in 2D and 3D, graph partitioning, MAXSAT, feature subset selection, forest management, groundwater remediation design, telecommunication network design, antenna design, and scheduling.
The tutorial Probabilistic Model-Building GAs will provide a gentle introduction to PMBGAs with an overview of major research directions in this area. Strengths and weaknesses of different PMBGAs will be discussed and suggestions will be provided to help practitioners to choose the best PMBGA for their problem.",
-
notes = "Also known as \cite{2002120} Distributed on CD-ROM at
GECCO-2011.
ACM Order Number 910112.",
