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After witnessing the rise of deep learning as automatic feature/pattern recognition over classic machine learning techniques, I had an insight that the more you automate at each level, the better the results, and I, therefore, turned my focus to neuroevolution.

I have been reading neuroevolution publications with the same desire to automate at every level.

Do genetic algorithms also evolve themselves? Do they get better at searching through the solution space for each generation over time?

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In principle, yes, you can also evolve the genetic algorithm (or, in general, evolutionary algorithm), i.e. you can evolve its operations (such as the mutation and cross-over) and hyper-parameters (such as the size of the population or mutation rate). For example, you could use genetic programming to evolve the cross-over operation of a genetic algorithm. However, these genetic operators and hyper-parameters are usually designed and determined by a human and do not change during the evolution process. Nevertheless, there is a framework for evolutionary computation in which reproduction and mutation can also evolve, known as auto-constructive evolution, and there are other examples in the literature of meta-optimization methods applied to evolutionary algorithms, such as

More generally, the optimization of an optimization algorithm is known as meta-optimization and/or hyper-parameter optimization/tuning, so the idea of meta-optimization is not just restricted to evolutionary algorithms, but can be applied also to e.g. deep learning.

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A genetic algorithm is a class of evolutionary algorithms.

They do get better at searching through the solution possibilities for each trial (generation) over time because evolution usually starts from a population of randomly generated individuals, and is an iterative process. In each generation, the fitness of every individual in the population is evaluated. The more fit individuals are stochastically selected from the current population, and each individual's genome is modified to form a new generation thus getting better and better with each generation over time.

Evolution is defined as the gradual development of something, especially from a simple to a more complex form.

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    $\begingroup$ I think the OP originally meant if the actual algorithms, like the mutation algorithm, also evolves or not, not if genetic algorithms are a form of evolution. $\endgroup$ – nbro Nov 17 '18 at 23:17

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