GENETSKI ALGORITMI PDF

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You have to log in to leave a comment. Your browser does not allow JavaScript! Javascript is necessary for the proper functioning of this website. Please enable JavaScript or use a modern browser. Animiranje vsakega objekta oz. V tej nalogi so uporabljeni genetski algoritmi za izbiro optimalnih parametrov modela kolektivnega gibanja. Iz rezultatov je razvidno, da se lahko s tem programom izberejo optimalne nastavitve simulacije kolektivnega gibanja za uporabljen scenarij.

AddThis uses cookies that require your consent. Edit consent In the production of animated films, problems frequently arise in scenes with groups of moving objects. Animating each object or member of the group individually can be very time consuming, which is why simulations that calculate all the movements are normally used in practice.

If group members need to be aware of other members and their surroundings, the most appropriate model for simulating the movement is a model of flocking behaviour. Such simulations save a lot of time at the expense of precise control over individual group members. To achieve the desired movement of the group, flocking simulation settings are iteratively changed until a satisfactory result is achieved. This thesis tests and evaluates the use of genetic algorithms for selecting the optimal parameters of simulated flocking.

The aim was to simulate group movement without collisions between group members or with the surroundings to get an authentic animation of group members' behaviour. A programme for flocking simulations was implemented and tested on a short scenario. A group of objects scattered across a designated starting area had to pass an obstacle course to reach a final destination. The programme was run at different settings, and collisions and frame calculation times were recorded.

The results show that the programme was able to set optimal parameters for flocking simulations in the scenario. Measurements show that groups of up to members can be simulated without collisions using the programme. Additionally, the use of a genetic algorithm reduced the time needed to calculate the simulation.

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