In the classical binary genetic algorithm, although crossover within a building block (BB) does not always cause a decrease in fitness, any decrease in fitness results from the ...
Bayesian Networks are today used in various fields and domains due to their inherent ability to deal with uncertainty. Learning Bayesian Networks, however is an NP-Hard task [7]....
We propose a protocol for a local search and a genetic algorithm for the distributed traveling salesman problem (TSP). In the distributed TSP, information regarding the cost funct...
Visualization of multidimensional data by means of Multidimensional Scaling (MDS) is a popular technique of exploratory data analysis widely usable, e.g. in analysis of bio-medica...
This paper presents a novel approach to the problem of automatic music transcription using electronic synthesis with genetic algorithms. Although the problem is well known and di...
In this paper we report on a study in which genetic algorithms are applied to the analysis of noisy time-series signals, which is related to the problem of analyzing the motion ch...
One of the most controversial yet enduring hypotheses about what genetic algorithms (GAs) are good for concerns the idea that GAs process building-blocks. More specifically, it ha...
Selecting the most relevant factors from genetic profiles that can optimally characterize cellular states is of crucial importance in identifying complex disease genes and biomark...
Abstract. Addressing dynamic optimization problems has been a challenging task for the genetic algorithm community. Over the years, several approaches have been developed into gene...
Classification of microarray data requires the selection of a subset of relevant genes in order to achieve good classification performance. Several genetic algorithms have been d...