Manual counting of objects on images is a time-consuming task, therefore we can make us of algorithms that automate object counting. Such algorithms have many settings. We substitute the settings of algorithms, constants and routines with parameters. The value of parameters for individual images has to be optimized for optimal functioning. Manual settings are hard to handle, since the parameters are interconnected. Our goal is to find an optimization algorithm, which will find a set of most suitable parameters for object counting out of the given description of the counting algorithm and the suitable domain, where this algorithm is going to be used. Due to a large number of possible complications (Cartesian product of domains of parameters) a complete overview of the space of parameters in impossible, therefore we used a genetic algorithm. The final product is a tool, which will be simple and useful for experts without any technical knowledge about concepts, hiding behind these algorithms.
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