The area of research within computer science that uses genetic algorithms is sometimes confused with computational evolutionary biology, but the two areas are not necessarily related.

* Fast SAT Solver-simple but fast implementation of SAT solver based on genetic algorithms

*" Disruption " in Schema ( genetic algorithms )

It is a specialization of genetic algorithms ( GA ) where each individual is a computer program.

Inspired by the biological concept and usefulness of genotypes, computer science employs simulated phenotypes in genetic programming and evolutionary algorithms.

He became known in the 1990s for his research on the use of genetic algorithms to evolve neural networks using three dimensional cellular automata inside field programmable gate arrays.

" During this 8 year span he and his fellow researchers published a series of papers in which they discussed the use of genetic algorithms to evolve neural structures inside 3D cellular automata.

TSP is a touchstone for many general heuristics devised for combinatorial optimization such as genetic algorithms, simulated annealing, Tabu search, ant colony optimization, river formation dynamics ( see swarm intelligence ) and the cross entropy method.

In some problems, it is hard or even impossible to define the fitness expression ; in these cases, a simulation may be used to determine the fitness function value of a phenotype ( e. g., computational fluid dynamics is used to determine the air resistance of a vehicle whose shape is encoded as the phenotype ), or even interactive genetic algorithms are used.

Although Crossover and Mutation are known as the main genetic operators, it is possible to use other operators such as regrouping, colonization-extinction, or migration in genetic algorithms.

:" Because highly fit schemata of low defining length and low order play such an important role in the action of genetic algorithms, we have already given them a special name: building blocks.

There are several criticisms of the use of a genetic algorithm compared to alternative optimization algorithms:

Diversity is important in genetic algorithms ( and genetic programming ) because crossing over a homogeneous population does not yield new solutions.

* For specific optimization problems and problem instances, other optimization algorithms may find better solutions than genetic algorithms ( given the same amount of computation time ).

The question of which, if any, problems are suited to genetic algorithms ( in the sense that such algorithms are better than others ) is open and controversial.

The notion of real-valued genetic algorithms has been offered but is really a misnomer because it does not really represent the building block theory that was proposed by John Henry Holland in the 1970s.

Parallel implementations of genetic algorithms come in two flavours.

Coarse-grained parallel genetic algorithms assume a population on each of the computer nodes and migration of individuals among the nodes.

Fine-grained parallel genetic algorithms assume an individual on each processor node which acts with neighboring individuals for selection and reproduction.

Other variants, like genetic algorithms for online optimization problems, introduce time-dependence or noise in the fitness function.

Genetic algorithms with adaptive parameters ( adaptive genetic algorithms, AGAs ) is another significant and promising variant of genetic algorithms.

In artificial intelligence, genetic programming ( GP ) is an evolutionary algorithm-based methodology inspired by biological evolution to find computer programs that perform a user-defined task.

This category includes a great variety of general metaheuristic methods, such as simulated annealing, tabu search, A-teams, and genetic programming, that combine arbitrary heuristics in specific ways.

Tree-like representations are explored in genetic programming and graph-form representations are explored in evolutionary programming ; a mix of both linear chromosomes and trees is explored in gene expression programming.

Genetic programming often uses tree-based internal data structures to represent the computer programs for adaptation instead of the list structures typical of genetic algorithms.

* Santa Fe Trail problem, a genetic programming exercise

John R. Koza is a computer scientist and a former consulting professor at Stanford University, most notable for his work in pioneering the use of genetic programming for the optimization of complex problems.

* Evolutionary programming-Similar to genetic programming, but the structure of the program is fixed and its numerical parameters are allowed to evolve.

* Gene expression programming-Like genetic programming, GEP also evolves computer programs but it explores a genotype-phenotype system, where computer programs of different sizes are encoded in linear chromosomes of fixed length.

* Neuroevolution-Similar to genetic programming but the genomes represent artificial neural networks by describing structure and connection weights.

In the novel Halo: Cryptum, the term geas is used to describe a Forerunner method of programming genetic memories or commands into a living creature.

Evolutionary programming was introduced by Lawrence J. Fogel in the US, while John Henry Holland called his method a genetic algorithm.

Also in the early nineties, a fourth stream following the general ideas had emerged – genetic programming.

These terminologies denote the field of evolutionary computing and consider evolutionary programming, evolution strategies, genetic algorithms, and genetic programming as sub-areas.

In artificial intelligence, stochastic programs work by using probabilistic methods to solve problems, as in simulated annealing, stochastic neural networks, stochastic optimization, genetic algorithms, and genetic programming.

Since 2005 there has been interest in using the performance offered by GPUs for evolutionary computation in general, and for accelerating the fitness evaluation in genetic programming in particular.

The method was published in 1987 as one of the first papers in the emerging field that later became known as genetic programming.

As an application of these general principles, social conservatives in many countries generally: favor the pro-life position in opposing euthanasia, embryonic stem cell research, and abortion ; oppose both eugenics ( inheritable genetic modification ) and human enhancement ( transhumanism ) while supporting bioconservatism ; support abstinence-only education, school prayers, gun ownership and defining marriage as the union of one man and one woman ; support the continued prohibition of recreational or medically non-beneficial drugs ; oppose prostitution and brothels, polygamy, gay adoption, premarital sex, and non-marital sex ; and object to pornography and what they consider to be indecency and promiscuity.

Phylogeographic analyses have also played an important role in defining evolutionary significant units ( ESU ), a unit of conservation below the species level that is often defined on unique geographic distribution and mitochondrial genetic patterns.

Identifying the locations of genes and other genetic control elements is often described as defining the biological " parts list " for the assembly and normal operation of an organism.

After the biochemical biological and genetic characteristics of IL-2 became known, Shinpei Kasakura's group performed a series of experiments defining BF almost twenty years after its first description.

In genetic algorithms as the defining length of a solution increases so does the susceptibility of the solution to disruption due to mutation or cross-over.

Monsanto v. Schmeiser was at times portrayed as part of the process of legally defining the bounds of new biotechnologies, including genetic engineering and ownership of higher lifeforms.

Commonly studied concepts in dialectology include the problem of mutual intelligibility in defining languages and dialects ; situations of diglossia, where two dialects are used for different functions ; dialect continua including a number of partially mutually intelligible dialects ; and pluricentrism, where what is essentially a single genetic language exists as two or more standard varieties.

The main challenge in using genetic algorithms is in defining the fitness criteria.