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{{Short description|Types of approximate algorithm}}
'''Soft computing''' is an umbrella term used to describe types of [[algorithm]]s that produce approximate solutions to unsolvable high-level problems in computer science. Typically, traditional hard-computing algorithms heavily rely on concrete data and [[Mathematical Models and Methods in Applied Sciences|mathematical models]] to produce solutions to problems. Soft computing was coined in the late 20th century.<ref>{{Cite journal |last=Zadeh |first=Lotfi A. |date=March 1994 |title=Fuzzy logic, neural networks, and soft computing |journal=Communications of the ACM |language=en |volume=37 |issue=3 |pages=77–84 |doi=10.1145/175247.175255 |issn=0001-0782|doi-access=free }}</ref> During this period, revolutionary research in three fields greatly impacted soft computing. Fuzzy logic is a computational paradigm that entertains the uncertainties in data by using levels of truth rather than rigid 0s and 1s in binary. Next, neural networks which are computational models influenced by human brain functions. Finally, evolutionary computation is a term to describe groups of algorithm that mimic natural processes such as evolution and natural selection.
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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 journal |title=Model Compression and Acceleration for Deep Neural Networks: The Principles, Progress, and Challenges |url=https://ieeexplore.ieee.org/document/8253600/;jsessionid=Es-8JJ2aTxyDbz-ZeAW6ojB2bGom7NU413NP86MhLqTbzB3fmAGf!-668841979 |access-date=2023-11-11 |journal=IEEE Signal Processing Magazine| date=2018 | doi=10.1109/MSP.2017.2765695 | last1=Cheng | first1=Yu | last2=Wang | first2=Duo | last3=Zhou | first3=Pan | last4=Zhang | first4=Tao | volume=35 | issue=1 | pages=126–136 | bibcode=2018ISPM...35a.126C | url-access=subscription }}</ref>
Neural networks revolve around [[perceptron]]s, which are [[artificial neuron]]s 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 |date=6 October 2021 |language=en-us}}</ref>
Through training, the network handles input and output data streams and adjusts parameters according to the provided information. Neural networks help make soft computing extraordinarily flexible and capable of handling high-level problems.
In soft computing, neural networks aid in pattern recognition, predictive modeling, and data analysis. They are also used in [[image recognition]], [[natural language processing]], [[speech recognition]], and [[system]]s.<ref name=":1" /><ref name=":3">{{Cite journal |title=Comprehensive Review of Artificial Neural Network Applications to Pattern Recognition
=== Evolutionary computation ===
[[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 algorithm|evolutionary algorithms]]. Evolutionary computation consists of algorithms that mimic natural selection, such as [[genetic algorithm]]s, [[genetic programming]], [[Evolution strategy|evolution strategies]] 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.
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Due to their dynamic versatility, soft computing models are precious tools that confront complex real-world problems. They are applicable in numerous industries and research fields:
Soft computing fuzzy logic and neural networks help with pattern recognition, image processing, and computer vision. Its versatility is vital in [[natural language processing]] as it helps decipher human emotions and language. They also aid in data mining and [[Predictive analytics|predictive analysis]] by obtaining priceless insights from enormous datasets. Soft computing helps optimize solutions from energy, [[financial forecast]]s, environmental and biological data modeling, and anything that deals with or requires models.<ref name=":4" /><ref>{{Cite journal |title=Industrial applications of soft computing: a review |url=https://ieeexplore.ieee.org/document/949483/;jsessionid=xdS8IFFQN8YRhXQajnUBK1GxF5Fzj_edYcUsqEW5vE3xWwb3XJ8G!-1911429853 |access-date=2023-11-11 |journal=Proceedings of the IEEE| date=2001 | doi=10.1109/5.949483 | last1=Dote | first1=Y. | last2=Ovaska | first2=S.J. | volume=89 | issue=9 | pages=1243–1265 | bibcode=2001IEEEP..89.1243D | url-access=subscription }}</ref>
Within the medical field, soft computing is revolutionizing disease detection, creating plans to treat patients and models of [[Health care|healthcare]].<ref name=":3" />
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== Challenges and limitations ==
Soft computing methods such as neural networks and fuzzy models are complicated and may need clarification. Sometimes, it takes effort to understand the logic behind neural network algorithms' decisions, making it challenging for a user to adopt them. In addition, it takes valuable, costly resources to feed models extensive data sets, and sometimes it is impossible to acquire the computational resources necessary. There are also significant hardware limitations which limits the computational power.<ref name=":2" />
== References ==
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