Question: What types of mutation are usually selected against?
About the article "Evolution", section "Biased mutation".

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Mutation is the ultimate source of genetic variation in the form of new alleles.
Mutation can result in several different types of change in DNA sequences; these can either have no effect, alter the product of a gene, or prevent the gene from functioning.
Studies in the fly "Drosophila melanogaster" suggest that if a mutation changes a protein produced by a gene, this will probably be harmful, with about 70 percent of these mutations having damaging effects, and the remainder being either neutral or weakly beneficial.
Mutations can involve large sections of DNA becoming duplicated, usually through genetic recombination.
These duplications are a major source of raw material for evolving new genes, with tens to hundreds of genes duplicated in animal genomes every million years.
Most genes belong to larger families of genes of shared ancestry.
Novel genes are produced by several methods, commonly through the duplication and mutation of an ancestral gene, or by recombining parts of different genes to form new combinations with new functions.
Here, domains act as modules, each with a particular and independent function, that can be mixed together to produce genes encoding new proteins with novel properties.
For example, the human eye uses four genes to make structures that sense light: three for color vision and one for night vision; all four arose from a single ancestral gene.
Another advantage of duplicating a gene (or even an entire genome) is that this increases redundancy; this allows one gene in the pair to acquire a new function while the other copy performs the original function.
Other types of mutation occasionally create new genes from previously noncoding DNA.
In addition to being a major source of variation, mutation may also function as a mechanism of evolution when there are different probabilities at the molecular level for different mutations to occur, a process known as mutation bias.
If two genotypes, for example one with the nucleotide G and another with the nucleotide A in the same position, have the same fitness, but mutation from G to A happens more often than mutation from A to G, then genotypes with A will tend to evolve.
Different insertion vs. deletion mutation biases in different taxa can lead to the evolution of different genome sizes.
Developmental or mutational biases have also been observed in morphological evolution.
For example, according to the phenotype-first theory of evolution, mutations can eventually cause the genetic assimilation of traits that were previously induced by the environment.
Mutation bias effects are superimposed on other processes.
If selection would favor either one out of two mutations, but there is no extra advantage to having both, then the mutation that occurs the most frequently is the one that is most likely to become fixed in a population.
Mutations leading to the loss of function of a gene are much more common than mutations that produce a new, fully functional gene.
Most loss of function mutations are selected against.
But when selection is weak, mutation bias towards loss of function can affect evolution.
For example, pigments are no longer useful when animals live in the darkness of caves, and tend to be lost.
This kind of loss of function can occur because of mutation bias, and/or because the function had a cost, and once the benefit of the function disappeared, natural selection leads to the loss.
Loss of sporulation ability in a bacterium during laboratory evolution appears to have been caused by mutation bias, rather than natural selection against the cost of maintaining sporulation ability.
When there is no selection for loss of function, the speed at which loss evolves depends more on the mutation rate than it does on the effective population size, indicating that it is driven more by mutation bias than by genetic drift. 
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.
Mutation alters one or more gene values in a chromosome from its initial state.
In mutation, the solution may change entirely from the previous solution.
Hence GA can come to better solution by using mutation.
Mutation occurs during evolution according to a user-definable mutation probability.
This probability should be set low.
If it is set too high, the search will turn into a primitive random search.
The classic example of a mutation operator involves a probability that an arbitrary bit in a genetic sequence will be changed from its original state.
A common method of implementing the mutation operator involves generating a random variable for each bit in a sequence.
This random variable tells whether or not a particular bit will be modified.
This mutation procedure, based on the biological point mutation, is called single point mutation.
Other types are inversion and floating point mutation.
When the gene encoding is restrictive as in permutation problems, mutations are swaps, inversions and scrambles.
The purpose of mutation in GAs is preserving and introducing diversity.
Mutation should allow the algorithm to avoid local minima by preventing the population of chromosomes from becoming too similar to each other, thus slowing or even stopping evolution.
This reasoning also explains the fact that most GA systems avoid only taking the fittest of the population in generating the next but rather a random (or semi-random) selection with a weighting toward those that are fitter.
For different genome types, different mutation types are suitable:  Bit string mutation :The mutation of bit strings ensue through bit flips at random positions.
:Example: :The probability of a mutation of a bit is formula_1, where formula_2 is the length of the binary vector.
Thus, a mutation rate of formula_3 per mutation and individual selected for mutation is reached.
Flip Bit This mutation operator takes the chosen genome and inverts the bits.
Boundary This mutation operator replaces the genome with either lower or upper bound randomly.
This can be used for integer and float genes.
Non-Uniform The probability that amount of mutation will go to 0 with the next generation is increased by using non-uniform mutation operator.
It keeps the population from stagnating in the early stages of the evolution.
It tunes solution in later stages of evolution.
This mutation operator can only be used for integer and float genes.
Uniform This operator replaces the value of the chosen gene with a uniform random value selected between the user-specified upper and lower bounds for that gene.
This mutation operator can only be used for integer and float genes.
Gaussian This operator adds a unit Gaussian distributed random value to the chosen gene.
If it falls outside of the user-specified lower or upper bounds for that gene,the new gene value is clipped.
This mutation operator can only be used for integer and float genes. 
  "For the biological term, see: Gene mutation analysis".
Mutation testing (or "Mutation analysis" or "Program mutation") is used to design new software tests and evaluate the quality of existing software tests.
Mutation testing involves modifying a program's source code or byte code in small ways.
Each mutated version is called a "mutant" and tests detect and reject mutants by causing the behavior of the original version to differ from the mutant.
This is called "killing" the mutant.
Test suites are measured by the percentage of mutants that they kill.
New tests can be designed to kill additional mutants.
Mutants are based on well-defined "mutation operators" that either mimic typical programming errors (such as using the wrong operator or variable name) or force the creation of valuable tests (such as driving each expression to zero).
The purpose is to help the tester develop effective tests or locate weaknesses in the test data used for the program or in sections of the code that are seldom or never accessed during execution.
Most of this article is about "program mutation", in which the program is modified.
A more general definition of "mutation analysis" is using well-defined rules defined on syntactic structures to make systematic changes to software artifacts.
Mutation analysis has been applied to other problems, but is usually applied to testing.
So "mutation testing" is defined as using mutation analysis to design new software tests or to evaluate existing software tests.
Thus, mutation analysis and testing can be applied to design models, specifications, databases, tests, XML, and other types of software artifacts, although program mutation is the most common. 
In multicellular organisms with dedicated reproductive cells, mutations can be subdivided into germ line mutations, which can be passed on to descendants through their reproductive cells, and somatic mutations (also called acquired mutations), which involve cells outside the dedicated reproductive group and which are not usually transmitted to descendants.
A germline mutation gives rise to a "constitutional mutation" in the offspring, that is, a mutation that is present in every cell.
A constitutional mutation can also occur very soon after fertilisation, or continue from a previous constitutional mutation in a parent.
The distinction between germline and somatic mutations is important in animals that have a dedicated germ line to produce reproductive cells.
However, it is of little value in understanding the effects of mutations in plants, which lack dedicated germ line.
The distinction is also blurred in those animals that reproduce asexually through mechanisms such as budding, because the cells that give rise to the daughter organisms also give rise to that organisms germ line.
A new mutation that was not inherited from either parent is called a "de novo" mutation.
Diploid organisms (e.g., humans) contain two copies of each gene — a paternal and a maternal allele.
Based on the occurrence of mutation on each chromosome, we may classify mutations into three types.
A heterozygous mutation is a mutation of only one allele.
A homozygous mutation is an identical mutation of both the paternal and maternal alleles.
Compound heterozygous mutations or a genetic compound comprises two different mutations in the paternal and maternal alleles.
A wildtype or homozygous non-mutated organism is one in which neither allele is mutated. 
Many mutation operators have been explored by researchers.
Here are some examples of mutation operators for imperative languages:  Statement deletion.
Replace each boolean subexpression with "true" and "false".
Replace each arithmetic operation with another, e.g.
codice_5 with codice_6, codice_7 and codice_8.
Replace each boolean relation with another, e.g.
codice_9 with codice_10, codice_11 and codice_12.
Replace each variable with another variable declared in the same scope (variable types must be compatible).
mutation score = number of mutants killed / total number of mutantsThese mutation operators are also called traditional mutation operators.
There are also mutation operators for object-oriented languages, for concurrent constructions, complex objects like containers, etc.
Operators for containers are called "class-level" mutation operators.
For example the muJava tool offers various class-level mutation operators such as Access Modifier Change, Type Cast Operator Insertion, and Type Cast Operator Deletion.
Mutation operators have also been developed to perform security vulnerability testing of programs