<ul><li>Maintaining a population of different solutions</li><li>Optimizing a single solution. </li></ul>

Main uses of 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."

Evolutionary algorithms

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.

Producing human intelligence

Cons <ul><li>It requires a considerable amount of computation.</li><li>Most people want to solve a particular problem. Neuroevolution might get you to a creative solution but could lead you astray before it puts you on the right path.</li></ul>Pros<ul><li>Other AI, like reinforcement learning, can lead to short-terms gains, but will eventually get stuck in a rut. Random exploration in the steppingstone principle can beat the best algorithms. </li><li>Open-ended exploration might lead to human-level intelligence via paths we would never have anticipated.</li></ul>

Pros and Cons of 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.

Neuroevolution

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.

The steppingstone principle

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.

Pursuing objectives can be problematic

Stanley's realization led to what he calls the steppingstone principle - and, with it, a way of designing algorithms that more fully embraces the endlessly creative potential of biological evolution. Evolutionary algorithms have been around for a long time.