Abstract
The aim of the paper is to propose and evaluate an agent-based population learning algorithm generating, by the prototype selection, a representative training dataset of the required size. It is assumed that prototypes are selected from clusters. The process of selection is executed by a team of agents, which execute various local search procedures and cooperate to find-out a solution to the considered problem. Rules for agent cooperation are defined within working strategies. In this paper influence of two different strategies and the population size on performance of the algorithm is investigated.
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