Genetik Algoritma

Genetik Algoritma Optimizasyon Problemlerinde Kullanılır
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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). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on biologically inspired operators such as mutation, crossover and selection.John Holland introduced genetic algorithms in 1960 based on the concept of Darwin’s theory of evolution; his student David E. Goldberg further extended GA in 1989.

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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.

A typical genetic algorithm requires:

- a genetic representation of the solution domain,
- a fitness function to evaluate the solution domain.

Genetik Algoritma İle Tüm Olasılıkların Değerlendirildiği Algoritma Arasındaki Fark Nedir?

Genetik Algoritma (Genetic Algorithm) deneme yanılma üzerine kuruludur. Bir fitness (uygunluk) metodu kullanılarak daha optimal sonuç elde edilmeye çalışılır. Bu yüzden daha kısa sürede sonuç bulabilme ihtimali vardır. Exhaustive arama ise tüm ihtimalleri denediği için çok daha uzun sürebilir.

Genetik Algoritma ve Evrimsel Algoritma Arasındaki Fark Nedir?
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1. An evolutionary algorithm (AE) is an algorithm that has a (set of) solution(s) and by mutating them somehow (crossover is here also seen as "mutating"), you eventually end up with better solution(s).
2. A genetic algorithm (GA) supports the concept of a crossover where two or more "solutions" produce new solutions.