<abstract xmlns="http://www.w3.org/1999/xhtml"><p> We propose a new linkage learning genetic algorithm called the Factor Graph based Genetic Algorithm (FGGA). In the FGGA, a factor graph is used to encode the underlying dependencies between variables of the problem. In order to learn the factor graph from a population of potential solutions, a symmetric non-negative matrix factorization is employed to factorize the matrix of pair-wise dependencies. To show the performance of the FGGA, encouraging experimental results on different separable problems are provided as support for the mathematical analysis of the approach. The experiments show that FGGA is capable of learning linkages and solving the optimization problems in polynomial time with a polynomial number of evaluations.</p></abstract>

We propose a new linkage learning genetic algorithm called the Factor Graph based Genetic Algorithm (FGGA). In the FGGA, a factor graph is used to encode the underlying dependencies between variables of the problem. In order to learn the factor graph from a population of potential solutions, a symmetric non-negative matrix factorization is employed to factorize the matrix of pair-wise dependencies. To show the performance of the FGGA, encouraging experimental results on different separable problems are provided as support for the mathematical analysis of the approach. The experiments show that FGGA is capable of learning linkages and solving the optimization problems in polynomial time with a polynomial number of evaluations.

A factor graph based genetic algorithm

Department of Computer Engineering Iran University of Science and Technology, Narmak, Tehran, Iran

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"A factor graph based genetic algorithm"}

We propose a new linkage learning genetic algorithm called the Factor Graph based Genetic Algorithm (FGGA). In the FGGA, a factor graph is used to encode the...

A factor graph based genetic algorithm

Publié en ligne: 25 sept. 2014

Pages: 621 - 633

Reçu: 23 juin 2013

DOI: https://doi.org/10.2478/amcs-2014-0045

Mots clés
optimization problems, genetic algorithms, estimation of distribution algorithms, factor graph, matrix factorization

© by B. Hoda Helmi

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

A factor graph based genetic algorithm

Publié en ligne: 25 sept. 2014

Pages: 621 - 633

Reçu: 23 juin 2013

DOI: https://doi.org/10.2478/amcs-2014-0045

Mots clésoptimization problems, genetic algorithms, estimation of distribution algorithms, factor graph, matrix factorization

© by B. Hoda Helmi

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Mots clés
optimization problems, genetic algorithms, estimation of distribution algorithms, factor graph, matrix factorization