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- Genetic algorithms

Quality control and genetic algorithms

The combination of quality control and genetic algorithms led to novel solutions of complex quality control design and optimization problems. Quality is the degree to which a set of inherent character

Multi expression programming

Multi Expression Programming (MEP) is an evolutionary algorithm for generating mathematical functions describing a given set of data. MEP is a Genetic Programming variant encoding multiple solutions i

Fitness function

A fitness function is a particular type of objective function that is used to summarise, as a single figure of merit, how close a given design solution is to achieving the set aims. Fitness functions

Stochastic universal sampling

Stochastic universal sampling (SUS) is a technique used in genetic algorithms for selecting potentially useful solutions for recombination. It was introduced by James Baker. SUS is a development of fi

Chromosome (genetic algorithm)

In genetic algorithms, a chromosome (also sometimes called a genotype) is a set of parameters which define a proposed solution to the problem that the genetic algorithm is trying to solve. The set of

Holland's schema theorem

Holland's schema theorem, also called the fundamental theorem of genetic algorithms, is an inequality that results from coarse-graining an equation for evolutionary dynamics. The Schema Theorem says t

Population-based incremental learning

In computer science and machine learning, population-based incremental learning (PBIL) is an optimization algorithm, and an estimation of distribution algorithm. This is a type of genetic algorithm wh

GATTO

No description available.

Genetic fuzzy systems

In computer science and operations research, Genetic fuzzy systems are fuzzy systems constructed by using genetic algorithms or genetic programming, which mimic the process of natural evolution, to id

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).

Mutation (genetic algorithm)

Mutation is a genetic operator used to maintain genetic diversity from one generation of a population of genetic algorithm chromosomes to the next. It is analogous to biological mutation. The classic

List of genetic algorithm applications

This is a list of genetic algorithm (GA) applications.

Weasel program

The weasel program or Dawkins' weasel is a thought experiment and a variety of computer simulations illustrating it. Their aim is to demonstrate that the process that drives evolutionary systems—rando

Schema (genetic algorithms)

A schema (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 string positions. Schemata are a spec

Selection (genetic algorithm)

Selection is the stage of a genetic algorithm in which individual genomes are chosen from a population for later breeding (using the crossover operator). A generic selection procedure may be implement

Inheritance (genetic algorithm)

In genetic algorithms, inheritance is the ability of modeled objects to mate, mutate (similar to biological mutation), and propagate their problem solving genes to the next generation, in order to pro

Fitness proportionate selection

Fitness proportionate selection, also known as roulette wheel selection, is a genetic operator used in genetic algorithms for selecting potentially useful solutions for recombination. In fitness propo

Premature convergence

In genetic algorithms, the term of premature convergence means that a population for an optimization problem converged too early, resulting in being . In this context, the parental solutions, through

Tournament selection

Tournament selection is a method of selecting an individual from a population of individuals in a genetic algorithm. Tournament selection involves running several "tournaments" among a few individuals

Genetic memory (computer science)

In computer science, genetic memory refers to an artificial neural network combination of genetic algorithm and the mathematical model of sparse distributed memory. It can be used to predict weather p

Santa Fe Trail problem

The Santa Fe Trail problem is a genetic programming exercise in which artificial ants search for food pellets according to a programmed set of instructions. The layout of food pellets in the Santa Fe

Genetic operator

A genetic operator is an operator used in genetic algorithms to guide the algorithm towards a solution to a given problem. There are three main types of operators (mutation, crossover and selection),

Edge recombination operator

The edge recombination operator (ERO) is an operator that creates a path that is similar to a set of existing paths (parents) by looking at the edges rather than the vertices. The main application of

Truncation selection

In animal and plant breeding, truncation selection is a standard method in selective breeding in selecting animals to be bred for the next generation. Animals are ranked by their phenotypic value on s

Defining length

In genetic algorithms and genetic programming defining length L(H) is the maximum distance between two defining symbols (that is symbols that have a fixed value as opposed to symbols that can take any

Fitness approximation

Fitness approximation aims to approximate the objective or fitness functions in evolutionary optimization by building up machine learning models based on data collected from numerical simulations or p

Parallel metaheuristic

Parallel metaheuristic is a class of techniques that are capable of reducing both the numerical effort and the run time of a metaheuristic. To this end, concepts and technologies from the field of par

Genetic programming

In artificial intelligence, genetic programming (GP) is a technique of evolving programs, starting from a population of unfit (usually random) programs, fit for a particular task by applying operation

Mating pool

A mating pool is a concept used in evolutionary computation, which refers to a family of algorithms used to solve optimization and search problems. The mating pool is formed by candidate solutions tha

Crossover (genetic algorithm)

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

Neuroevolution of augmenting topologies

NeuroEvolution of Augmenting Topologies (NEAT) is a genetic algorithm (GA) for the generation of evolving artificial neural networks (a neuroevolution technique) developed by Kenneth Stanley and Risto

Linkage disequilibrium score regression

In statistical genetics, linkage disequilibrium score regression (LDSR or LDSC) is a technique that aims to quantify the separate contributions of polygenic effects and various confounding factors, su

Evolver (software)

Evolver is a software package that allows users to solve a wide variety of optimization problems using a genetic algorithm. Launched in 1989, it was the first commercially available genetic algorithm

Genetic algorithms in economics

Genetic algorithms have increasingly been applied to economics since the pioneering work by John H. Miller in 1986. It has been used to characterize a variety of models including the cobweb model, the

Search-based software engineering

Search-based software engineering (SBSE) applies metaheuristic search techniques such as genetic algorithms, simulated annealing and tabu search to software engineering problems. Many activities in so

Fly algorithm

The Fly Algorithm is a type of cooperative coevolution based on the Parisian approach. The Fly Algorithm has first been developed in 1999 in the scope of the application of Evolutionary algorithms to

HyperNEAT

Hypercube-based NEAT, or HyperNEAT, is a generative encoding that evolves artificial neural networks (ANNs) with the principles of the widely used NeuroEvolution of Augmented Topologies (NEAT) algorit

Reward-based selection

Reward-based selection is a technique used in evolutionary algorithms for selecting potentially useful solutions for recombination. The probability of being selected for an individual is proportional

Promoter based genetic algorithm

The promoter based genetic algorithm (PBGA) is a genetic algorithm for neuroevolution developed by F. Bellas and R.J. Duro in the (GII) at the University of Coruña, in Spain. It evolves variable size

Gene expression programming

In computer programming, gene expression programming (GEP) is an evolutionary algorithm that creates computer programs or models. These computer programs are complex tree structures that learn and ada

Genetic algorithm scheduling

The genetic algorithm is an operational research method that may be used to solve scheduling problems in production planning.

Clonal selection algorithm

In artificial immune systems, clonal selection algorithms are a class of algorithms inspired by the clonal selection theory of acquired immunity that explains how B and T lymphocytes improve their res

Cultural algorithm

Cultural algorithms (CA) are a branch of evolutionary computation where there is a knowledge component that is called the belief space in addition to the population component. In this sense, cultural

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