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 all solutions is known as the population. The chromosome is often represented as a binary string, although a wide variety of other data structures are also used.
The design of the chromosome and its parameters is by necessity specific to the problem to be solved. Traditionally, chromosomes are represented in binary as strings of 0s and 1s, however other encodings are also possible; almost any representation which allows the solution to be represented as a finite-length string can be used. Finding a suitable representation of the problem domain for a chromosome is an important consideration, as a good representation will make the search easier by limiting the search space; similarly, a poorer representation will allow a larger search space. The mutation operator and crossover operator employed by the genetic algorithm must also take into account the chromosome's design.
In the field of artificial intelligence, a genetic algorithm (GA) is a search heuristic that mimics the process of natural selection. This heuristic (also sometimes called a metaheuristic) is routinely used to generate useful solutions to optimization and search problems. Genetic algorithms belong to the larger class of evolutionary algorithms (EA), which generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover.
In a genetic algorithm, a population of candidate solutions (called individuals, creatures, or phenotypes) to an optimization problem is evolved toward better solutions. Each candidate solution has a set of properties (its chromosomes or genotype) which can be mutated and altered; traditionally, solutions are represented in binary as strings of 0s and 1s, but other encodings are also possible.
The evolution usually starts from a population of randomly generated individuals, and is an iterative process, with the population in each iteration called a generation. In each generation, the fitness of every individual in the population is evaluated; the fitness is usually the value of the objective function in the optimization problem being solved. The more fit individuals are stochastically selected from the current population, and each individual's genome is modified (recombined and possibly randomly mutated) to form a new generation. The new generation of candidate solutions is then used in the next iteration of the algorithm. Commonly, the algorithm terminates when either a maximum number of generations has been produced, or a satisfactory fitness level has been reached for the population.
Genetic may refer to:
In linguistics, genetic relationship is the usual term for the relationship which exists between languages that are members of the same language family. The term genealogical relationship is sometimes used to avoid confusion with the unrelated use of the term in biological genetics. Languages that possess genetic ties with one another belong to the same linguistic grouping, known as a language family. These ties are established through use of the comparative method of linguistic analysis.
Two languages are considered to be genetically related if one is descended from the other or if both are descended from a common ancestor. For example, Italian is descended from Latin. Italian and Latin are therefore said to be genetically related. Spanish is also descended from Latin. Therefore, Spanish and Italian are genetically related. In a similar way, Swedish and Norwegian are genetically related through the North Germanic branch of the Indo-European language family.
In mathematics and computer science, an algorithm (i/ˈælɡərɪðəm/ AL-gə-ri-dhəm) is a self-contained step-by-step set of operations to be performed. Algorithms exist that perform calculation, data processing, and automated reasoning.
The words 'algorithm' and 'algorism' come from the name al-Khwārizmī. Al-Khwārizmī (Persian: خوارزمي, c. 780-850) was a Persian mathematician, astronomer, geographer, and scholar.
An algorithm is an effective method that can be expressed within a finite amount of space and time and in a well-defined formal language for calculating a function. Starting from an initial state and initial input (perhaps empty), the instructions describe a computation that, when executed, proceeds through a finite number of well-defined successive states, eventually producing "output" and terminating at a final ending state. The transition from one state to the next is not necessarily deterministic; some algorithms, known as randomized algorithms, incorporate random input.
An algorithm is a self-contained step-by-step set of operations to be performed.
Algorithm may also refer to:
A medical algorithm is any computation, formula, statistical survey, nomogram, or look-up table, useful in healthcare. Medical algorithms include decision tree approaches to healthcare treatment (e.g., if symptoms A, B, and C are evident, then use treatment X) and also less clear-cut tools aimed at reducing or defining uncertainty.
Medical algorithms are part of a broader field which is usually fit under the aims of medical informatics and medical decision making. Medical decisions occur in several areas of medical activity including medical test selection, diagnosis, therapy and prognosis, and automatic control of medical equipment.
In relation to logic-based and artificial neural network-based clinical decision support system, which are also computer applications to the medical decision making field, algorithms are less complex in architecture, data structure and user interface. Medical algorithms are not necessarily implemented using digital computers. In fact, many of them can be represented on paper, in the form of diagrams, nomographs, etc.