Genetic Learning Algorithm (GA)

Context:
- It can be implemented by a Genetic Learning System.
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Example(s):
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See: Genetic Operation, DNA, Natural Selection, Metaheuristic, Optimization (Mathematics), Mutation (Genetic Algorithm), Selection (Genetic Algorithm), Crossover (Genetic Algorithm).

References

(Sammut, 2017) ⇒ Claude Sammut.(2017). "Genetic and Evolutionary Algorithms". In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA
- QUOTE: There are many variations of genetic algorithms (GA). Here, wedescribe a simple scheme to introduce some of the key terms in genetic and evolutionary algorithms. See the main entry on Evolutionary Algorithms for references to specific methods.
  In genetic learning, we assume that there is a population of individuals, each of which represents a candidate problem solver for a given task. GAs can be thought of as a family of general purpose search methods that are capable of solving a broad range of problems from optimization and scheduling to robot control. Like evolution, genetic algorithms test each individual from the population and only the fittest survive to reproduce for the next generation. The algorithm creates new generations until at least one individual is found that can solve the problem adequately.

(Wikipedia, 2014) ⇒ http://en.wikipedia.org/wiki/genetic_algorithm Retrieved:2014-11-20.
- In the computer science 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.
  Genetic algorithms find application in bioinformatics, phylogenetics, computational science, engineering, economics, chemistry, manufacturing, mathematics, physics, pharmacometrics and other fields.

http://www.genetic-programming.com/gpanimatedtutorial.html
- Genetic programming is a domain-independent method that genetically breeds a population of computer programs to solve a problem. Specifically, genetic programming iteratively transforms a population of computer programs into a new generation of programs by applying analogs of naturally occurring genetic operations. The genetic operations include crossover (sexual recombination), mutation, reproduction, gene duplication, and gene deletion.

(Mitchell, 1998) ⇒ Melanie Mitchell. 1998. "An Introduction to Genetic Algorithms." MIT press.

(Mitchell et al., 1993) ⇒ Melanie Mitchell, John Holland, and Stephanie Forrest. 1993. "When will a Genetic Algorithm Outperform Hill Climbing." Advances in Neural Information Processing Systems 6.

(Mitchell et al., 1991) ⇒ Melanie Mitchell, John H. Holland, and Stephanie Forrest. 1991. "The Royal Road for Genetic Algorithms: Fitness landscapes and GA performance." Los Alamos National Lab., NM (United States).