Abstract
Manipulating a huge amount of data sets makes the mechanism of searching very difficult to find the bestfeatures combinations in phylogenetic tree construction. This article has proposed a binary particle swarm optimizationalgorithm that randomly selects chloroplast genes to construct phylogenetic trees. Also, a simple mutationoperator the article has suggested to speed up the search and facilitate the search process to find the best chloroplast genescombinations. These combinations can be evaluated according to the bootstrap values of all the nodes of theconstructed tree.
Highlights
Feature selection in DNA sequences had become an important research tool in biological experiments
Adaptive mutation operators have been proposed in many articles [2] and [3]
We find that if we compute the fitness twice: before the mutation step and after it like many articles [2]
Summary
Feature selection in DNA sequences had become an important research tool in biological experiments. Many algorithms have been proposed for feature selection. Some of these algorithms depend on the mechanism of filtering each element separately depending on specific measures. The Binary swarm optimization technique has been considered as one of the most well-known algorithms for feature selection. Adaptive mutation operators have been proposed in many articles [2] and [3]. These operators make the swarm very capable of finding the optimal solution in local search. Applying BPSO is not enough to find the intended results, so a mutation operator has been proposed in order to control the mechanism of local searching, where each element in the particle can make a difference. This article ends with a conclusion section, in which the article is summarized
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