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What is an algorithm? How computers know what to do with data
An algorithm is a set of instructions
The instructions tell a computer how to transform a set of facts into useful information.
The facts are data. The useful information is knowledge for people, instructions for machines or input for another algorithm. Typical examples are sorting sets of numbers or finding routes through maps.
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Key Ideas
Neuroevolution
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.
Evolutionary algorithms
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."
The steppingstone principle
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.