POPULATION-BASED MULTI-AGENT APPROACH TO SOLVING MACHINE LEARNING PROBLEMS
Ireneusz Czarnowski, Piotr Jędrzejowicz · Cybernetics & Systems · 2011
The purpose of this article is to present the application of the A-team approach to solving some machine learning problems belonging to the supervised and unsupervised learning classes. Because the above problems are computationally hard, it is proposed to take advantage of the robustness and flexibility of population-based methods combined with the efficiency of multi-agent systems integrated within the A-team concept. The main part of the article summarizes the experiences of the authors gained while developing various A-teams and includes some examples of population-based multi-agent algorithms for solving problems from the machine learning domain. It can be concluded that population-based multi-agent algorithms can be competitive in comparison with other existing techniques for some machine learning problems.