Now showing items 1-6 of 6
New methods for generating populations in Markov network based EDAs: Decimation strategies and model-based template recombination
Methods for generating a new population are a fundamental component of estimation of distribution algorithms (EDAs). They serve to transfer the information contained in the probabilistic model to the new generated population. ...
The Linear Ordering Problem Revisited
The Linear Ordering Problem is a popular combinatorial optimisation problem which has been extensively addressed in the literature. However, in spite of its popularity, little is known about the characteristics of this ...
Using network mesures to test evolved NK-landscapes
In this paper we empirically investigate which are the structural characteristics that can help to predict the complexity of NK-landscape instances for estimation of distribution algorithms. To this end, we evolve instances ...
A review on Estimation of Distribution Algorithms in Permutation-based Combinatorial Optimization Problems
Estimation of Distribution Algorithms (EDAs) are a set of algorithms that belong to the field of Evolutionary Computation. Characterized by the use of probabilistic models to represent the solutions and the dependencies ...
Analyzing limits of effectiveness in different implementations of estimation of distribution algorithms
Conducting research in order to know the range of problems in which a search algorithm is effective constitutes a fundamental issue to understand the algorithm and to continue the development of new techniques. In this ...
Extending Distance-based Ranking Models In Estimation of Distribution Algorithms
Recently, probability models on rankings have been proposed in the field of estimation of distribution algorithms in order to solve permutation-based combinatorial optimisation problems. Particularly, distance-based ranking ...