Sociology

Tournament selection

Population-based incremental learning

Population

Mathematics

Biology

Genetic algorithm

Gene

Operating system

Algorithm

Mathematical optimization

Machine learning

Crossover

Demography

Quality control and genetic algorithms

Artificial intelligence

Fitness function

Computer science

Mutation

Process (computing)

Chromosome

Meta-optimization

Genetics

Cultural algorithm

Genetic representation

Selection (genetic algorithm)

Fitness proportionate selection

Genetic Algorithm (GA) may be attributed as method for optimizing the search tool for difficult problems based on genetics selection principle. In additions to Optimization it also serves the purpose of machine learning and for Research and development. It is analogous to biology for chromosome generation with variables such as selection, crossover and mutation together constituting genetic operations which would be applicable on a random population initially. GA aims to yield solutions for the consecutive generations. The extent of success in individual production is directly in proportion to fitness of solution which is represented by it, thereby ensuring that quality in successive generations will be better. The process is concluded once an GA is most suitable for the issues that need optimization associated with some computable system.. John Holland may be regarded as funding father of original genetic algorithm and is attributed to year 1970's as funding date. Additionally a random search method represented by Charles Darwin for a defined search space in order to effetely solve a problem. In this paper, what is genetic algorithm and its basic workflow is discussed how a genetic algorithm work and what are the process is included in this is also discussed. Further, the features and application of genetic algorithm are mentioned in the paper.

Genetic Algorithm- A Literature Review