Soft computing is an umbrella term used to describe types of algorithms 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 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 ethical artificial intelligence.

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 algorithms 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.

Fuzzy logic 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, conjunction, and disjunction, 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 are computational models that attempt to mimic the structure and functioning of the human brain. 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 perceptrons, which are artificial neurons structured in layers. Like the human brain, these interconnected nodes process information using complicated mathematical operations.