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Evolutionary computation from computer science is a family of algorithms for global optimization inspired by biological evolution, and the subfield of artificial intelligence and soft computing studying these algorithms.
Differential evolution – Based on vector differences and is therefore primarily suited for numerical optimization problems. Coevolutionary algorithm – Similar to genetic algorithms and evolution strategies, but the created solutions are compared on the basis of their outcomes from interactions with other solutions.
Evolutionary programming is an evolutionary algorithm, where a share of new population is created by mutation of previous population without crossover. [ 1 ] [ 2 ] Evolutionary programming differs from evolution strategy ES( μ + λ {\displaystyle \mu +\lambda } ) in one detail. [ 1 ]
Neuroevolution, or neuro-evolution, is a form of artificial intelligence that uses evolutionary algorithms to generate artificial neural networks (ANN), parameters, and rules. [1] It is most commonly applied in artificial life , general game playing [ 2 ] and evolutionary robotics .
The competing conventions problem arises when there is more than one way of representing information in a phenotype. For example, if a genome contains neurons A, B and C and is represented by [A B C], if this genome is crossed with an identical genome (in terms of functionality) but ordered [C B A] crossover will yield children that are missing information ([A B A] or [C B C]), in fact 1/3 of ...
A language model AI created proteins as good as ones honed over a million years of evolution. The implications are staggering. AI Has Successfully Imitated Human Evolution—and Might Do It Even ...
Evolutionary algorithms is a sub-field of evolutionary computing. Evolution strategies (ES, see Rechenberg, 1994) evolve individuals by means of mutation and intermediate or discrete recombination. ES algorithms are designed particularly to solve problems in the real-value domain. [58]
In addition, machine learning has been applied to systems biology problems such as identifying transcription factor binding sites using Markov chain optimization. [2] Genetic algorithms, machine learning techniques which are based on the natural process of evolution, have been used to model genetic networks and regulatory structures. [2]