A STEM-friendly deep dive into how evolutionary computation uses randomness and Darwinian principles to solve hard optimization problems. We’ll trace its origins—from Alan Turing’s early ideas to the three branches (evolution strategies, evolutionary programming, genetic algorithms) and the rise of genetic programming—then explore real-world applications in engineering and AI, plus how these algorithms model evolution itself.

Note:  This podcast was AI-generated, and sometimes AI can make mistakes.  Please double-check any critical information.

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