It works by using a population of potential solutions to a problem, repeatedly selecting and breeding the most successful candidates until the ultimate solution emerges after a number of generations. And then there’s the approach called a genetic algorithm.Ī genetic algorithm (GA) uses principles from evolution to solve problems. Reinforcement learning uses rewards-based concepts, improving over time. Neural networks are great for finding patterns in data, resulting in predictive capabilities that are truly impressive. One of the great things about machine learning is that there are so many different approaches to solving problems. A Practical Example of a Genetic Algorithm
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