science and operations research, Genetic fuzzy systems are fuzzy systems constructed by using genetic algorithms or genetic programming, which mimic the process...

systems Evolutionary neural networks Genetic fuzzy systems Rough fuzzy hybridization Reinforcement learning with fuzzy, neural, or evolutionary methods as...

so-called adaptive "genetic" software systems, with the ultimate goal of building "self-learning" fuzzy-control systems. These systems can be employed to...

In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to...

An adaptive neuro-fuzzy inference system or adaptive network-based fuzzy inference system (ANFIS) is a kind of artificial neural network that is based...

"Linear genetic programming" is unrelated to "linear programming". Linear genetic programming (LGP) is a particular method of genetic programming wherein...

component of evolutionary algorithms (EA), such as genetic programming, evolution strategies or genetic algorithms. An EA is a metaheuristic that reproduces...

A genetic operator is an operator used in evolutionary algorithms (EA) to guide the algorithm towards a solution to a given problem. There are three main...

thereby the affinity or specificity of interactions. Genetic fuzzy systems use algorithms or genetic programming which simulate natural evolutionary processes...

evolutionary computation, also called recombination, is a genetic operator used to combine the genetic information of two parents to generate new offspring...

Expert Systems with Applications. 183: 115338. doi:10.1016/j.eswa.2021.115338. ISSN 0957-4174. Brameier, Markus (2004). "On Linear Genetic Programming"...

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...

(2003). "Evolutionary Algorithms". Advanced Fuzzy Systems Design and Applications. Studies in Fuzziness and Soft Computing. Vol. 112. Physica-Verlag...

a class of genetic adaptive systems (PhD). Ann Arbor, MI: University of Michigan. hdl:2027.42/4507. Michalewicz, Zbigniew (1996). Genetic Algorithms +...

Genetic programming (GP) is an evolutionary algorithm, an artificial intelligence technique mimicking natural evolution, which operates on a population...

Nicholas (2008), "A simple multi-chromosome genetic algorithm optimization of a Proportional-plus-Derivative Fuzzy Logic Controller", NAFIPS 2008 - 2008 Annual...

Selection is a genetic operator in an evolutionary algorithm (EA). An EA is a metaheuristic inspired by biological evolution and aims to solve challenging...

evolution for function optimization". Biennial Conference of the North American Fuzzy Information Processing Society (NAFIPS). pp. 519–523. doi:10.1109/NAFIPS...

is a genetic operator used to maintain genetic diversity of the chromosomes of a population of an evolutionary algorithm (EA), including genetic algorithms...

Rothlauf, Franz (2002). Representations for Genetic and Evolutionary Algorithms. Studies in Fuzziness and Soft Computing. Vol. 104. Heidelberg: Physica-Verlag...

survive. Genetic algorithms deliver methods to model biological systems and systems biology that are linked to the theory of dynamical systems, since they...

Classification Using Hybrid Genetic Algorithms". Intelligent Interactive Multimedia Systems and Services. Smart Innovation, Systems and Technologies. Vol. 11...

(pl.: schemata) is a template in computer science used in the field of genetic algorithms that identifies a subset of strings with similarities at certain...

find prime integrals of dynamical systems, represent variable topology artificial neural networks and more. Genetic programming Gene expression programming...

Fuzzy clustering (also referred to as soft clustering or soft k-means) is a form of clustering in which each data point can belong to more than one cluster...

fitness describes the flow of a population and natural selection by taking genetic operators into account. A normal fitness function fits to a problem, while...

Laboratory, and was coauthor of a report in 1974 on "Program-Understanding Systems". Inspired by work on Eurisko, Lenat proposed that mutations may be highly...

algorithms, which serves as an optimization technique. It uses the major genetic operators mutation, recombination and selection of parents. The 'evolution...

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...

to fitness, and introduce genetic variation using one or more genetic operators. Their use in artificial computational systems dates back to the 1950s where...