Curated from: quantamagazine.org
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Neuroevolution is a form of artificial intelligence. It is a meta-algorithm, an algorithm for designing algorithms. It adopts the principles of biological evolution in order to design smarter algorithms. Eventually, the algorithms get pretty good at their job.
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Traditionally, evolutionary algorithms are used to solve specific problems. For instance, the ability to control a two-legged robot. Solutions that perform the best on some metrics are selected to produce offspring.
In spite of successes, these algorithms are more computationally intensive than approaches such as "deep learning."
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It goes beyond traditional evolutionary approaches. It explains innovation. Instead of optimizing for a specific goal, it embraces the creative exploration of a diverse population of solutions.
The steppingstone’s potential can be seen by analogy with biological evolution: feathers likely evolved for insulation and only later became handy for flight.
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Biological evolution is the only system to produce human intelligence.
If we want algorithms that can navigate the physical and social world as we do, we need to imitate nature's tactics. Instead of hard-coding for specific metrics, we must let a population of solutions blossom that may discover an indirect path or a set of stepping stones to allow them to evolve better than if they'd received those skills directly.
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Pursuing specific goals can be a hindrance to reaching those objectives.
Kenneth Stanley, a computer scientist, hoped to show that by following ideas in interesting directions, algorithms can produce a diversity of results and solve problems. Thus, ignoring an objective can get you to the solution faster than pursuing it. He showed this through an approach named novelty search.
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