Category: 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
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
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