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=== Neural networks ===
[[Neural network]]s are computational models that attempt to mimic the structure and functioning of the [[human brain]]. While computers typically use [[Boolean algebra|binary logic]] to solve problems, neural networks attempt to provide solutions for complicated problems by enabling systems to think human-like, which is essential to soft computing.<ref name=":2">{{Cite web |title=Model Compression and Acceleration for Deep Neural Networks: The Principles, Progress, and Challenges {{!}} IEEE Journals & Magazine {{!}} IEEE Xplore |url=https://ieeexplore.ieee.org/document/8253600/;jsessionid=Es-8JJ2aTxyDbz-ZeAW6ojB2bGom7NU413NP86MhLqTbzB3fmAGf!-668841979 |access-date=2023-11-11 |website=ieeexplore.ieee.org}}</ref>
Neural networks revolve around [[Perceptron|perceptrons]], which are [[Artificial neuron|artificial neurons]] structured in layers. Like the human brain, these interconnected nodes process information using complicated mathematical operations.<ref>{{Cite web |title=What are Neural Networks? {{!}} IBM |url=https://www.ibm.com/topics/neural-networks |access-date=2023-11-11 |website=www.ibm.com |language=en-us}}</ref>
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[[Evolutionary computation]] is a field in soft computing that uses the principles of [[natural selection]] and [[evolution]] to solve complicated problems. It promotes the discovery of diverse solutions within a solution space, encouraging near-perfect solutions. It finds satisfactory solutions by using computational models and types of evolutionary algorithms. Evolutionary computation consists of algorithms that mimic natural selection, such as [[Genetic algorithm|genetic algorithms]], [[genetic programming]], and [[evolutionary programming]]. These algorithms use [[Crossover (genetic algorithm)|crossover]], [[Mutation (genetic algorithm)|mutation]], and [[Selection (genetic algorithm)|selection]].<ref>{{Cite web |date=2017-06-29 |title=Genetic Algorithms |url=https://www.geeksforgeeks.org/genetic-algorithms/ |access-date=2023-11-11 |website=GeeksforGeeks |language=en-US}}</ref>
Crossover, or recombination, exchanges data between nodes to diversify data and handle more outcomes. [[Mutation]] is a genetic technique that helps prevent the premature conclusion to a suboptimal solution by diversifying an entire range of solutions. It helps new optimal solutions in solution sets that help the overall optimization process. Selection is an operator that chooses which solution from a current population fits enough to transition to the next phase. These drive genetic programming to find optimal solutions by ensuring the survival of only the fittest solutions in a set.
In soft computing, evolutionary computation helps applications of [[data mining]] (using large sets of data to find patterns), [[robotics]], optimizing, and engineering methods.<ref name=":1" /><ref name=":0" />
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