Soft computing
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Soft computing is an umbrella term used to describe types of
algorithm In mathematics and computer science, an algorithm () is a finite sequence of Rigour#Mathematics, mathematically rigorous instructions, typically used to solve a class of specific Computational problem, problems or to perform a computation. Algo ...
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 to produce solutions to problems. Soft computing was coined in the late 20th century. 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 Artificial intelligence (AI) is the capability of computer, computational systems to perform tasks typically associated with human intelligence, such as learning, reasoning, problem-solving, perception, and decision-making. It is a field of re ...
and
machine learning Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of Computational statistics, statistical algorithms that can learn from data and generalise to unseen data, and thus perform Task ( ...
, 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 ethical artificial intelligence.Ibrahim, Dogan. "An overview of soft computing." Procedia Computer Science 102 (2016): 34-38.


History

The development of soft computing dates back to the late 20th century. In 1965, 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 In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to g ...
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 Pattern recognition is the task of assigning a class to an observation based on patterns extracted from data. While similar, pattern recognition (PR) is not to be confused with pattern machines (PM) which may possess PR capabilities but their p ...
. 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 Big data primarily refers to data sets that are too large or complex to be dealt with by traditional data processing, data-processing application software, software. Data with many entries (rows) offer greater statistical power, while data with ...
, including engineering, medicine, social sciences, and finance.


Computational techniques


Fuzzy logic

Fuzzy logic Fuzzy logic is a form of many-valued logic in which the truth value of variables may be any real number between 0 and 1. It is employed to handle the concept of partial truth, where the truth value may range between completely true and completely ...
is an aspect of computing that handles approximate reasoning. Typically, 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. Unlike 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 In logic, negation, also called the logical not or logical complement, is an operation (mathematics), operation that takes a Proposition (mathematics), proposition P to another proposition "not P", written \neg P, \mathord P, P^\prime or \over ...
, conjunction, and
disjunction In logic, disjunction (also known as logical disjunction, logical or, logical addition, or inclusive disjunction) is a logical connective typically notated as \lor and read aloud as "or". For instance, the English language sentence "it is ...
, which handle membership between data sets. 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.


Neural networks

Neural network A neural network is a group of interconnected units called neurons that send signals to one another. Neurons can be either biological cells or signal pathways. While individual neurons are simple, many of them together in a network can perfor ...
s are computational models that attempt to mimic the structure and functioning of the
human brain The human brain is the central organ (anatomy), organ of the nervous system, and with the spinal cord, comprises the central nervous system. It consists of the cerebrum, the brainstem and the cerebellum. The brain controls most of the activi ...
. While computers typically use 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. Neural networks revolve around
perceptron In machine learning, the perceptron is an algorithm for supervised classification, supervised learning of binary classification, binary classifiers. A binary classifier is a function that can decide whether or not an input, represented by a vect ...
s, which are
artificial neuron An artificial neuron is a mathematical function conceived as a model of a biological neuron in a neural network. The artificial neuron is the elementary unit of an ''artificial neural network''. The design of the artificial neuron was inspired ...
s structured in layers. Like the human brain, these interconnected nodes process information using complicated mathematical operations. 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 Computer vision tasks include methods for acquiring, processing, analyzing, and understanding digital images, and extraction of high-dimensional data from the real world in order to produce numerical or symbolic information, e.g. in the form o ...
,
natural language processing Natural language processing (NLP) is a subfield of computer science and especially artificial intelligence. It is primarily concerned with providing computers with the ability to process data encoded in natural language and is thus closely related ...
,
speech recognition Speech recognition is an interdisciplinary subfield of computer science and computational linguistics that develops methodologies and technologies that enable the recognition and translation of spoken language into text by computers. It is also ...
, and
system A system is a group of interacting or interrelated elements that act according to a set of rules to form a unified whole. A system, surrounded and influenced by its open system (systems theory), environment, is described by its boundaries, str ...
s.


Evolutionary computation

Evolutionary computation 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. In technical terms ...
is a field in soft computing that uses the principles of
natural selection Natural selection is the differential survival and reproduction of individuals due to differences in phenotype. It is a key mechanism of evolution, the change in the Heredity, heritable traits characteristic of a population over generation ...
and
evolution Evolution is the change in the heritable Phenotypic trait, characteristics of biological populations over successive generations. It occurs when evolutionary processes such as natural selection and genetic drift act on genetic variation, re ...
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 algorithms (EA) reproduce essential elements of the biological evolution in a computer algorithm in order to solve "difficult" problems, at least Approximation, approximately, for which no exact or satisfactory solution methods are k ...
. Evolutionary computation consists of algorithms that mimic natural selection, such as
genetic algorithm In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to g ...
s,
genetic programming Genetic programming (GP) is an evolutionary algorithm, an artificial intelligence technique mimicking natural evolution, which operates on a population of programs. It applies the genetic operators selection (evolutionary algorithm), selection a ...
,
evolution strategies Evolution strategy (ES) from computer science is a subclass of evolutionary algorithms, which serves as an optimization (mathematics), optimization technique. It uses the major genetic operators mutation (evolutionary algorithm), mutation, recomb ...
and
evolutionary programming Evolutionary programming is an evolutionary algorithm, where a share of new population is created by mutation of previous population without crossover. Evolutionary programming differs from evolution strategy ES(\mu+\lambda) in one detail. All in ...
. These algorithms use crossover,
mutation In biology, a mutation is an alteration in the nucleic acid sequence of the genome of an organism, virus, or extrachromosomal DNA. Viral genomes contain either DNA or RNA. Mutations result from errors during DNA or viral replication, ...
, and
selection Selection may refer to: Science * Selection (biology), also called natural selection, selection in evolution ** Sex selection, in genetics ** Mate selection, in mating ** Sexual selection in humans, in human sexuality ** Human mating strat ...
. Crossover, or recombination, exchanges data between nodes to diversify data and handle more outcomes.
Mutation In biology, a mutation is an alteration in the nucleic acid sequence of the genome of an organism, virus, or extrachromosomal DNA. Viral genomes contain either DNA or RNA. Mutations result from errors during DNA or viral replication, ...
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 Data mining is the process of extracting and finding patterns in massive data sets involving methods at the intersection of machine learning, statistics, and database systems. Data mining is an interdisciplinary subfield of computer science and ...
(using large sets of data to find patterns),
robotics Robotics is the interdisciplinary study and practice of the design, construction, operation, and use of robots. Within mechanical engineering, robotics is the design and construction of the physical structures of robots, while in computer s ...
, optimizing, and engineering methods.


Hybrid intelligence systems

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 Data analysis is the process of inspecting, Data cleansing, cleansing, Data transformation, transforming, and Data modeling, modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. Da ...
. Hybrid intelligence systems help overcome the limitations of individual AI approaches to improve performance, accuracy, and adaptability to address dynamic problems. It advances soft computing capabilities in data analysis, pattern recognition, and systems.


Applications

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 Natural language processing (NLP) is a subfield of computer science and especially artificial intelligence. It is primarily concerned with providing computers with the ability to process data encoded in natural language and is thus closely related ...
as it helps decipher human emotions and language. They also aid in data mining and predictive analysis by obtaining priceless insights from enormous datasets. Soft computing helps optimize solutions from energy,
financial forecast A financial forecast is an estimate of future financial outcomes for a company or project, usually applied in budgeting, capital budgeting and/or valuation. Depending on context, the term may also refer to listed company (quarterly) earnings gui ...
s, environmental and biological data modeling, and anything that deals with or requires models. Within the medical field, soft computing is revolutionizing disease detection, creating plans to treat patients and models of
healthcare Health care, or healthcare, is the improvement or maintenance of health via the preventive healthcare, prevention, diagnosis, therapy, treatment, wikt:amelioration, amelioration or cure of disease, illness, injury, and other disability, physic ...
.


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.


References

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