Soft computing: Difference between revisions

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{{Short description|Computing that is tolerantTypes of imprecision, uncertainty, partial truth, andapproximate approximationalgorithm}}

'''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.

In the context of [[artificial intelligence]] and [[machine learning]], soft computing provides tools to handle real-world uncertainties. Its methods supplement preexisting methods for better solutions. Today, the combination with artificial intelligence has led to hybrid intelligence systems that merge various computational algorithms. Expanding the applications of artificial intelligence, soft computing leads to robust solutions. Key points include tackling ambiguity, flexible learning, grasping intricate data, real-world applications, and [[Ethics of artificial intelligence|ethical artificial intelligence]].<ref name="Procedia">Ibrahim, Dogan. "An overview of soft computing." Procedia Computer Science 102 (2016): 34-38.</ref><ref name=":1">{{Cite book |last=Kecman |first=Vojislav |url=https://books.google.com/books?id=W5SAhUqBVYoC&dq=neural+networks+in+soft+computing&pg=PP1 |title=Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models |date=2001 |publisher=MIT Press |isbn=978-0-262-11255-0 |language=en}}</ref>

[[File:Soft_Computing_pic.jpg|thumb|Concept illustration of ''Soft computing'']]

'''Soft computing''' is a set of [[algorithms]],<ref name=":0">

{{Citation|title=Soft Computing Techniques|date=2016|url=https://www.cambridge.org/core/books/soft-computing-in-electromagnetics/soft-computing-techniques/3220054EB1CB76AB1C34CF1F86660E69|work=Soft Computing in Electromagnetics: Methods and Applications|pages=9–44|editor-last=Choudhury|editor-first=Balamati|place=Cambridge|publisher=Cambridge University Press|doi=10.1017/CBO9781316402924.003|isbn=978-1-107-12248-2|access-date=2021-02-24|editor2-last=Jha|editor2-first=Rakesh Mohan}}

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including [[neural network]]s, [[fuzzy logic]], and [[genetic algorithm]]s.<ref>

{{Citation|last=Shukla|first=K. K.|title=CHAPTER 17 - Soft Computing Paradigms for Artificial Vision|date=2000-01-01|url=https://www.sciencedirect.com/science/article/pii/B9780126464900500202|work=Soft Computing and Intelligent Systems|pages=405–417|editor-last=Sinha|editor-first=NARESH K.|series=Academic Press Series in Engineering|place=San Diego|publisher=Academic Press|language=en|isbn=978-0-12-646490-0|access-date=2021-02-24|editor2-last=Gupta|editor2-first=MADAN M.}}

</ref>

These algorithms are tolerant of imprecision, uncertainty, partial truth and approximation.

It is contrasted with '''hard computing''': algorithms which find provably correct and [[optimal]] solutions to problems.

__TOC__

== History ==

The development of soft computing dates back to the late 20th century. In 1965, [[Lotfi A. Zadeh|Lotfi Zadeh]] introduced fuzzy logic, which laid the mathematical groundwork for soft computing. Between the 1960s and 1970s, evolutionary computation, the development of [[genetic algorithm]]s that mimicked biological processes, began to emerge. These models carved the path for models to start handling uncertainty. Although neural network research began in the 1940s and 1950s, there was a new demand for research in the 1980s. Researchers invested time to develop models for [[pattern recognition]]. Between the 1980s and 1990s, hybrid intelligence systems merged fuzzy logic, neural networks, and evolutionary computation that solved complicated problems quickly. From the 1990s to the present day, Models have been instrumental and affect multiple fields handling [[big data]], including engineering, medicine, social sciences, and finance.<ref>Chaturvedi, Devendra K. "Soft computing." Studies in Computational intelligence 103 (2008): 509-612.</ref><ref name=":0">{{Cite book |last1=Ram |first1=Mangey |url=https://books.google.com/books?id=qjL3DwAAQBAJ&dq=history%20of%20soft%20computing&pg=PA74 |title=Advanced Mathematical Techniques in Engineering Sciences |last2=Davim |first2=J. Paulo |date=2018-05-04 |publisher=CRC Press |isbn=978-1-351-37189-6 |language=en}}</ref>

The theory and techniques related to soft computing were first introduced in 1980s.<ref>{{Cite journal|last=Ibrahim|first=Dogan|date=2016-01-01|title=An Overview of Soft Computing|journal=Procedia Computer Science|series=12th International Conference on Application of Fuzzy Systems and Soft Computing, ICAFS 2016, 29–30 August 2016, Vienna, Austria|language=en|volume=102|pages=34–38|doi=10.1016/j.procs.2016.09.366|issn=1877-0509|doi-access=free}}</ref> The term "soft computing" was coined by [[Lotfi A. Zadeh]].<ref>{{Cite journal|last=Zadeh|first=Lotfi A.|date=1994-03-01|title=Fuzzy logic, neural networks, and soft computing|url=https://doi.org/10.1145/175247.175255|journal=Communications of the ACM|volume=37|issue=3|pages=77–84|doi=10.1145/175247.175255|s2cid=1824401 |issn=0001-0782}}</ref><ref name=":0" />

== Computational techniques ==

=== Fuzzy logic ===

[[Fuzzy logic]] is an aspect of computing that handles approximate reasoning. Typically, [[Boolean algebra|binary logic]] allows computers to make decisions on true or false reasons (0s and 1s); however, introducing fuzzy logic allows systems to handle the unknowns between 0 and 1.<ref name="Procedia" /><ref>{{Cite web |date=2018-04-10 |title=Fuzzy Logic {{!}} Introduction |url=https://www.geeksforgeeks.org/fuzzy-logic-introduction/ |access-date=2023-11-11 |website=GeeksforGeeks |language=en-US}}</ref>

Unlike [[Classical set theory|classical sets]] that allow members to be entirely within or out, fuzzy sets allow partial membership by incorporating "graduation" between sets. Fuzzy logic operations include [[negation]], conjunction, and [[Logical disjunction|disjunction]], which handle membership between data sets.<ref name=":0" />

Fuzzy rules are logical statements that map the correlation between input and output parameters. They set the rules needed to trace variable relationships linguistically, and they would not be possible without [[linguistic variables]]. Linguistic variables represent values typically not quantifiable, allowing uncertainties.<ref>Trillas, Enric, and Luka Eciolaza. "Fuzzy logic." Springer International Publishing. DOI 10 (2015): 978-3.</ref>

=== 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 |doi=10.1109/ACCESS.2019.2945545 |date=2019 |last1=Abiodun |first1=Oludare Isaac |last2=Kiru |first2=Muhammad Ubale |last3=Jantan |first3=Aman |last4=Omolara |first4=Abiodun Esther |last5=Dada |first5=Kemi Victoria |last6=Umar |first6=Abubakar Malah |last7=Linus |first7=Okafor Uchenwa |last8=Arshad |first8=Humaira |last9=Kazaure |first9=Abdullahi Aminu |last10=Gana |first10=Usman |journal=IEEE Access |volume=7 |pages=158820–158846 |bibcode=2019IEEEA...7o8820A |doi-access=free }}</ref>

=== 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.

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" />

== See also ==

* [[Emergence]]

* [[Synthetic intelligence]]

* [[Watson (computer)]]

=== Hybrid intelligence systems ===

== Notable journals ==

Hybrid intelligence systems combine the strengths of soft computing components to create integrated computational models. Artificial techniques such as fuzzy logic, neural networks, and evolutionary computation combine to solve problems efficiently. These systems improve judgment, [[troubleshooting]], and [[data analysis]]. Hybrid intelligence systems help overcome the limitations of individual AI approaches to improve performance, accuracy, and adaptability to address [[Dynamic problem (algorithms)|dynamic problems]]. It advances soft computing capabilities in data analysis, pattern recognition, and systems.<ref name=":4">{{Cite book |last=Medsker |first=Larry R. |url=https://books.google.com/books?id=EXngBwAAQBAJ&q=evolutionary&pg=PR13 |title=Hybrid Intelligent Systems |date=2012-12-06 |publisher=Springer Science & Business Media |isbn=978-1-4615-2353-6 |language=en}}</ref>

== Applications ==

* ''Soft Computing''<ref>{{Cite web|title=Soft Computing|url=https://www.springer.com/journal/500|url-status=live|access-date=2021-02-26|website=[[Springer Science+Business Media|Springer]]|language=en|issn=1432-7643}}</ref>

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:

*''Applied Soft Computing''<ref>{{Cite book|url=https://www.journals.elsevier.com/applied-soft-computing|title=Applied Soft Computing|publisher=[[Elsevier B.V.]]|issn=1568-4946}}</ref>

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>

==References==

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Within the medical field, soft computing is revolutionizing disease detection, creating plans to treat patients and models of [[Health care|healthcare]].<ref name=":3" />

== Challenges and limitations ==

[[Category:Soft computing]]

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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