| Summary: | In this report we have presented the results of a program which performs simula- tions of artificial bacteria with the ability to evolve different characteristics and behaviours through genetic algorithms. Over time unfit bacteria will die out, and the more fit bacteria will produce offspring with slightly mutated variants of it’s genetic code resembling the evolutionary process. The simulation does not follow the traditional macro-scale hand-picked sexual reproduction often used in genetic algorithms to produce optimal results, but it instead uses individ- ual asexual reproduction which more closely resembles how bacteria reproduce in nature. Furthermore we do not use traditional neural networks for decision making, but simple functions which directly map the bacterias inputs to their decisions. The purpose of this study was to observe whether bacteria with different initial starting populations would coevolve, and specialize into heterogeneous populations. Furthermore we have tried to analyze how the populations inter- act with each other and how changing the different parameters of the simulation would affect the populations. We have performed three separate experiments that differ in their initial conditions, one with pre-created and heterogeneous herbivores and carnivores, one with homogeneous omnivores, and one with bac- teria whose genetic values have been decided at random. The result of our experiments was that we observed coevolution in the bacteria, and that they would despite very different initial starting conditions always grow towards sta- ble heterogeneous populations with very few exceptions.
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