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2 Journal of Artificial Intelligence and Soft Computing Research

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Multi-population-based algorithm with an exchange of training plans based on population evaluation

Łapa Krystian, Cpałka Krzysztof, Kisiel-Dorohinicki Marek, Paszkowski Józef, Dębski Maciej, Le Van-Hung

Journal of Artificial Intelligence and Soft Computing Research

2022

Vol. 12, No. 4

239--253

Population Based Algorithms (PBAs) are excellent search tools that allow searching space of parameters defined by problems under consideration. They are especially useful when it is difficult to define a differentiable evaluation criterion. This applies, for example, to problems that are a combination of continuous and discrete (combinatorial) problems. In such problems, it is often necessary to select a certain structure of the solution (e.g. a neural network or other systems with a structure usually selected by the trial and error method) and to determine the parameters of such structure. As PBAs have great application possibilities, the aim is to develop more and more effective search formulas used in them. An interesting approach is to use multiple populations and process them with separate PBAs (in a different way). In this paper, we propose a new multi-population-based algorithm with: (a) subpopulation evaluation and (b) replacement of the associated PBAs subpopulation formulas used for their processing. In the simulations, we used a set of typical CEC2013 benchmark functions. The obtained results confirm the validity of the proposed concept.

A novel grid-based clustering algorithm

Starczewski Artur, Scherer Magdalena M., Książek Wojciech, Dębski Maciej, Wang Lipo

Journal of Artificial Intelligence and Soft Computing Research

2021

Vol. 11, No. 4

319--330

Data clustering is an important method used to discover naturally occurring structures in datasets. One of the most popular approaches is the grid-based concept of clustering algorithms. This kind of method is characterized by a fast processing time and it can also discover clusters of arbitrary shapes in datasets. These properties allow these methods to be used in many different applications. Researchers have created many versions of the clustering method using the grid-based approach. However, the key issue is the right choice of the number of grid cells. This paper proposes a novel grid-based algorithm which uses a method for an automatic determining of the number of grid cells. This method is based on the kdist function which computes the distance between each element of a dataset and its kth nearest neighbor. Experimental results have been obtained for several different datasets and they confirm a very good performance of the newly proposed method.