Evolutionary Computation (EC) is a fundamentally different learning method from Deep Learning (DL) and Reinforcement Learning (RL). Those methods are primarily based on improving existing solution through gradients on performance. Thus, learning is based on exploiting what we know: known examples and small successive changes to an existing solution. In contrast, EC is based on a parallel search in a population of solutions. Its main driver is exploration, i.e. modifying a diverse set of solutions systematically and often drastically, based on what is learned from the entire space of solutions.
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