We developed a simulation of artificial life with a colony of intelligent individuals (agents) by using a combination of neural networks, genetic algorithms, and ant colony optimization. Each agent has its own brain implemented as a neural network. Several agents form a colony which interacts with the environment by gathering and storing food. They communicate using pheromone trails. Through the processes of inheritance and mutation of agents' brain the colony can develop continuously. With simulation we gathered the information on the effectiveness of colonies with varying rates of mutation (from 0.01 to 0.10) and compared the results. The colonies with low mutation rates were overall less successful, while the colonies with high mutation rates were successful in developing only certain behaviours. The most successful colonies used the mutation rate of 0.05, which presents a good balance between creating new agents and copying the existing successful agents. The simulation allows adjustment of parameters and presents a good basis for further development and adding more complex agent behaviours.
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