What is Genetic Algorithm

A probabilistic search technique for attaining an optimum solution to combinatorial problems that works in the principles of genetics.

An iterative meta-heuristic based on the evolution of species, that handles a population of solutions of the optimization problem that have a survive probability proportional to the quality of the respective solution, and makes combinations of solutions based on crossover and mutation operators.

The concept of basic genetics is applied to form a meta-heuristic optimization search technique.

A systematic method used to solve search and optimization problems and apply to such problems the principles of biological evolution, namely, selection of the ‘fittest’, sexual reproduction (crossover) and mutation.

The approximation algorithm based on the evolutional process.

Search technique to find exact or approximate solution to search problems.

A heuristic method for finding solutions to an optimization problem that takes advantage of evolutionary principles; different possible solutions to the problem are iteratively subjected to “replication”, “mutation” and “selection” processes. In order to illuminate its general principles a simple instance of the method is described below. In the context of RNA folding, the genetic algorithm might start with a randomly generated set of conformations that are compatible with the RNA sequence being folded. Then, in each iteration of the algorithm, multiple copies of each conformation are made (the replication step); more copies are made for conformations with lower free energies. The copying process is not perfect, but it introduces “mutations”, which may involve the creation/destruction of base pairs or entire helices. Following replication and mutation, a subset of the resulting conformations is selected based on their free energies and subsequently subjected to the next round of replication, mutation, and selection. Eventually, the obtained conformations would be enriched for those with free energies approaching the lowest possible free energy for the RNA sequence being folded.

An algorithm that mimics the genetic concepts of natural selection, combination, selection, and inheritance.

A type of evolutionary computation algorithm in which candidate solutions are represented typically by vectors of integers or bit strings, that is, by vectors of binary values 0 and 1

Is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA).

A special type of evolutionary technique where the potential solutions are represented by means of chromosomes (usually either binary or real sets of values). Each gene (or set of genes) represents a variable or parameter within the global solution.

Stochastic global optimization method, based on biological evolution and inspired by Darwin’s theory of “survival of the fittest”.

A series of steps to allow the evolution of solutions to specific problems. It is inspired in biological evolution and particularly its genetic-molecular basis.

A genetic algorithm is a population-based metaheuristic algorithm that uses genetics-inspired operators to sample the solution space. This means that this algorithm applies some kind of genetic operators to a population of individuals (solutions) in order to evolve (improve) them throughout the generations (iterations).

Abstraction and implementation of evolutionary principles and theories in computational algorithms to search optimal solutions to a problem.

Genetic algorithm (GA), one of the most popularly used evolutionary tools among soft computing paradigm, is mainly devised to solve real world ill-defined, and imprecisely formulated problems requiring huge computation. It is the power of GA to introduce some heuristic methodologies to minimize the search space for optimal solution(s) without sticking at local optima. Due to the inherent power, GA becomes one of the most successful heuristic optimization algorithms. It is widely used to solve problems of diversified fields ranging from engineering to art.

Belongs to the larger class of evolutionary algorithms and is a search heuristic inspired on process of natural selection and is routinely used to generate useful solutions to optimization and search problems.

An algorithm that evaluates a function searching for an optimal solution with methods inspired by natural selection strategies.

Global search method based on a simile of the natural evolution.

A search algorithm to enable you to locate optimal binary strings by processing an initial random population of binary strings by performing operations.

It is a meta-heuristic technique inspired by the natural selection process.

A search technique used in computing to find exact or approximate solutions to optimization and search problems.

A search heuristic used in computing to find true or approximate solutions to global optimization problems.

It is a population-based search and optimization tool that works based on Darwin’s principle of natural selection.

A metaheuristic that explores a solution space via adaptive search procedures based on principles derived from natural evolution and genetics. Solutions are typically coded as strings of binary digits called chromosomes.

The basic idea behind genetic algorithm is to apply the principles of Darwin’s evolution theory. Briefly speaking, the algorithm is often done by the following procedure: 1) encoding of an initial population of chromosomes, i.e., representing solutions; 2) defining a fineness function; 3) evaluating the population by using genetic operations resulting in a new population; 4) decoding the result to obtain the solution of problem.

An evolutionary approach applied in systems based on gene property of human being.

Genetic Algorithms (GAs) are adaptive heuristic search algorithm premised on the evolutionary ideas of natural selection and genetic. The basic concept of GAs is designed to simulate processes in natural system necessary for evolution, specifically those that follow the principles first laid down by Charles Darwin of survival of the fittest. As such they represent an intelligent exploitation of a random search within a defined search space to solve a problem.

A genetic algorithm (abbreviated as GA) is a search technique used in computer science to approximate solutions to optimization and search problems.

It can be defined as heuristic search procedure that works on the principles of biological evolution.

Genetic Algorithm is a bio-inspired heuristic solution search algorithm based on the evolutionary ideas of natural selection and genetics in living organism.

It is a stochastic but not random method of search used for optimization or learning. Genetic algorithm is basically a search technique that simulates biological evolution during optimization process.

An optimization resource that uses interactive procedures to simulate the process of evolution of possible solutions populations to a particular problem. The process of evolution is random, but guided by a selection mechanism based on adaptation of individual structures. New structures are generated randomly with a given probability and included in the population. The result tends to be an increase in the adaptation of individuals to the environment and can result in an overall increase in fitness of the population with each new generation.

Is a search meta-heuristic that mimics the process of natural selection. This meta-heuristic routinely used to generate useful solutions to optimization and search problems. Genetic algorithms belong to the larger class of evolutionary algorithms which generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection and crossover.

A subclass of evolutionary computing that uses genetic operators such as mutation and crossover to evolve solutions to mimic natural evolution.

Genetic algorithm is a global solution search approach and based on the mechanics of natural selection and natural genetics.

A search technique that uses the concept of survival of the fittest to find an optimal or near optimal solution to a problem. Genetic algorithms use techniques inspired by evolutionary biology to generate new possible solutions known as offspring from an existing set of parent solutions. These recombination techniques include inheritance, selection, mutation, and crossover.

Class of algorithms used to find approximate solutions to difficult-to-solve problems, inspired and named after biological processes of inheritance, mutation, natural selection, and generic crossover. Genetic algorithms are a particular class of evolutionary algorithms.

A metaheuristic algorithms inspired on the biological process of evolution and natural selection. Genetic algorithms are known to generate high-quality solutions with low influence of local minimums or maximums, relying on computational equivalents of natural processes such as crossover, mutation and environment fitness.

Genetic algorithm is an adaptive heuristic search algorithm based on the evolutionary ideas of natural selection and genetics in living system.

GA is an adaptive heuristic search algorithm that models biological genetic evolution. It proved to be a strong optimizer that searches among a population of solutions, and showed flexibility in solving dynamic problems.

A search heuristic that generate solutions to optimization problems using techniques inspired by natural evolution, such as selection, crossover and mutation.

Genetic Algorithm is a population based adaptive evolutionary technique motivated by the natural process of survival of fittest, widely used as an optimization technique for large search spaces.

This is a type of optimization algorithm that is inspired by the process of natural selection and evolution. It is a search-based algorithm that uses principles of genetics and natural selection to find solutions to optimization problems. In a genetic algorithm, a population of potential solutions is generated, and each solution is evaluated based on a fitness function. The solutions with the highest fitness are used to generate new solutions through the process of selection, crossover, and mutation, in which parts of the high-fitness solutions are combined to create new, potentially better solutions. This process is repeated over several generations until a satisfactory solution is found or a stopping criterion is reached. Genetic algorithms are commonly used in a variety of fields, including engineering, finance, and gaming, to solve complex optimization problems that are difficult to solve with traditional optimization methods.

An algorithm for optimizing a property based on an evolutionary mechanism that uses replication, deletion, and mutation processes carried out over many generations. (Goldberg, D.E., 1989) (Fogel, L.J., Owens, A.J. & Walsh, M.A. 1966)

A genetic algorithm is a metaheuristic inspired by the process of natural selection to solve optimization problems.

An iterative optimization algorithm that works to minimize a given objective function by generating a random population and performing genetic operations to generate a new population.

An adaptive approach that provides a randomized, parallel, and global search based on the mechanics of natural selection and genetics in order to find solutions of a problem.

A special algorithmic optimization procedure, developed on the basis of simple hereditary property of animals and used for both of constrained and unconstrained problem. In Artificial Intelligence (AI), it is used as heuristic search also.

A heuristics algorithm that is based on the mechanism of natural selection and natural genetics.

A heuristic search method used in artificial intelligence and computing. It is used for finding optimized solutions to search problems based on the theory of natural selection and evolutionary biology.

It is an adaptive heuristic search algorithm based on the evolutionary ideas of natural selection and genetics in living system.

Genatic algorithm is a metaheuristic which is inspired by the process of natural selection corresponding to the larger class of evolutionary algorithms.

Technique to search exact or approximate solutions of optimization or search problem by using evolution-inspired phenomena such as selection, crossover, and mutation. Genetic algorithm is classified as global search algorithm

A method of evolutionary computation for problem solving. There are states also called sequences and a set of possibility final states. Methods of mutation are used on genetic sequences to achieve better sequences.

An algorithm that mimics the genetic concepts of natural selection, combination, selection, and inheritance.

Heuristic algorithm of search for solving optimization problem by means of random variations of parameters using approaches similar to natural selection.

An evolutionary algorithm which generates each individual from some encoded form known as “chromosomes” or “genome”.

A metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms.

Heuristic procedure that mimics evolution through natural selection.

General-purpose search algorithms that use principles by natural population genetics to evolve solutions to problems

It is a search technique that imitates the procedure of natural selection. Genetic algorithms are used to optimize different search problems.

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 (Wikipedia, 2015 AU156: The in-text citation "Wikipedia, 2015" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ; Mitchell, 1996 ).

A natural selection-based strategy for addressing both constrained and unconstrained optimization problems, which is based on the process that drives biological evolution, is used to solve this type of problem.

A genetic algorithm (GA) is a heuristic used to find approximate solutions to difficult-to-solve problems through application of the principles of evolutionary biology to computer science. Genetic algorithms use biologically-derived techniques such as inheritance, mutation, natural selection, and recombination (or crossover). Genetic algorithms are a particular class of evolutionary algorithms.

An artificially intelligent technique motivated by the genetic behavior of animals and capable of solving non-linear optimization problems.

Characterizes potential problem hypotheses using a binary string representation, and iterates a search space of potential hypotheses in an attempt to identify the best hypothesis, which is that which optimizes a predefined numerical measure, or fitness. GAs is, collectively, a subset of evolutionary algorithms.

An evolutionary algorithm-based methodology inspired by biological evolution to find computer programs that perform a user-defined task.

An algorithm that mimics the genetic concepts of natural selection, combination, selection, and inheritance.

A stochastic population-based global optimization technique that mimics the process of natural evolution.

It is a search algorithm which uses natural selection and the mechanisms of population genetics. It is used for solving the constrained & unconstrained optimization problems ( Holland, 1968 ).

canonical optimization method that emulate the evolution and inheritance.

An optimization scheme based on biological genetic evolutionary principles.

An evolutionary optimization algorithm based on the principles of genetics and the survival-of-the-fittest law of nature. Starting with a population of solutions, the algorithm applies crossover and mutation to the members of the population that best fit the objective function in order to obtain better fitting solutions.

A special type of evolutionary technique which represents the potential solutions of a problem within chromosomes (usually a collection of binary, natural or real values).

Is an algorithm that is created after the structure of genetics and the inter-breeding of DNA.

These are the search and optimization algorithms which are capable of searching large solution spaces to find the optimal solutions using the methods of natural selection.

A metaheuristic, which is inspired by the theory of Darvin on natural selection and evaluation.

A search technique used in computing to find exact or approximate solutions to optimization and search problems.

Is an optimization technique based on Darwin’s survival of the fittest hypothesis. It was developed after original work by John Holland in 1970s. In a GA, given a problem for which a closed-form solution is unidentified, or impossible to obtain with classical methods, an initial randomly generated population of possible solution is created. Its characteristics are then used in an equivalent string of genes that will be later recombined with genes from other individuals. Each solution is assimilated to an individual, who is evaluated and classified in relation with its closeness to the best, yet still unknown, solution to the problem. As in a biological system submitted to external constraints, the fittest members of the initial population are given better chances of reproducing and transmitting part of their genetic heritage to the next generation. A new population, or second generation, is then created by recombination of parental genes. It is expected that some members of this new population will have acquired the best characteristics of both parents and, being better adapted to the environmental conditions, will provide an improved solution to the problem. After it has replaced the original population, the new group is submitted to the same evaluation procedure, and later generates its own offspring’s. The process is repeated many times, until all members of a given generation share the same genetic heritage. From then on, there are virtually no differences between individuals. The members of these final generations, who are often quite different from their ancestors, possess genetic information that corresponds to the best solution to the optimization problem.

A probabilistic search technique for achieving an optimum solution to combinatorial problems that works in the principles of genetics.

Genetic Algorithms (GA) are a way of solving problems by mimicking the same processes mother nature uses. They use the same combination of selection, recombination and mutation to evolve a solution to a problem

Adaptive heuristic search algorithm based on the principle of natural selection and natural genetics. In order to arrive at optimal solution for design problems, the GA has been implemented so that the fundamental concepts of reproduction, chromosomal crossover, occasional mutation of genes and natural selection are reflected in the different stages of the genetic algorithm process. Although randomized, Genetic Algorithm is by no means random, instead they exploit historical information to steer the search into the region of better public presentation within the search distance. The process is initiated by selecting a number of candidate design variables either randomly or heuristically in order to create an initial population, which is then encouraged to evolve over generations to produce new designs, which are better or filter.

An evolutionary optimization algorithm and the most popular population-based search meta-heuristic algorithm.

An algorithm that simulates the natural evolutionary process, applied the generation of the solution of a problem. It is usually used to obtain the value of parameters difficult to calculate by other means (like for example the neural network weights). It requires the definition of a cost function

A heuristic optimisation technique, which is biologically inspired using to find a set of optimal solutions.

Genetic algorithms are evolutionary optimization methods motivated by biological phenomenon of natural selection and evolution.