![]() ![]() Since we want to evolve a population by using the best units, we need to define a fitness function. In addition to the genetic algorithm (step 3), here we go with more details about fitness function – what it is and how to define it. (for more details see replacement strategy below) when all units died, evaluate the current population to the next one by using genetic operators (for more details see fitness function below) for each unit calculate its fitness function to measure its quality let all units play the game simultaneously by using their own neural networks create initial population of 10 units (birds) with random neural networks Here are the main steps of our genetic algorithm implementation: It uses the same combination of selection, crossover and mutation to evolve initial random population. Genetic algorithm is a search-based optimization technique inspired by the process of natural selection and genetics. When we talked about machine learning algorithm, we said that a genetic algorithm is used to train and improve neural networks. ![]() The picture below shows the neural network architecture for this example:
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