GC: n
CT: A genetic algorithm is a search heuristic that is inspired by Charles Darwin’s theory of natural evolution. This algorithm reflects the process of natural selection where the fittest individuals are selected for reproduction in order to produce offspring of the next generation. The process of natural selection starts with the selection of fittest individuals from a population. They produce offspring which inherit the characteristics of the parents and will be added to the next generation. If parents have better fitness, their offspring will be better than parents and have a better chance at surviving. This process keeps on iterating and at the end, a generation with the fittest individuals will be found. This notion can be applied for a search problem. We consider a set of solutions for a problem and select the set of best ones out of them.
S: MMagazine – https://bit.ly/2GOuLWf (last access: 19 December 2018)
N: 1. – genetic (adj): from Greek genetikos from genesis “origin”; modern sense of “pertaining to genetics or genes”.
– algorithm (n): “Arabic system of computation”; from French algorithme (under mistaken connection with Greek arithmos “number”; from Medieval Latin algorismus, a mangled transliteration of Arabic al-Khwarizmi “native of Khwarazm” (modern Khiva in Uzbekistan), surname of the mathematician whose works introduced sophisticated mathematics to the West. The earlier form in Middle English was algorism (early 13c.), from Old French.
- Acronym: GA
- Frequent use in plural: genetic algorithms
2. Method for solving both constrained and unconstrained optimization problems that is based on natural selection, the process that drives biological evolution. The genetic algorithm repeatedly modifies a population of individual solutions.
3. The following outline summarizes how the genetic algorithm works:
– The algorithm begins by creating a random initial population.
– The algorithm then creates a sequence of new populations. At each step, the algorithm uses the individuals in the current generation to create the next population. To create the new population, the algorithm performs the following steps:
- Scores each member of the current population by computing its fitness value. These values are called the raw fitness scores.
- Scales the raw fitness scores to convert them into a more usable range of values. These scaled values are called expectation values.
- Selects members, called parents, based on their expectation.
- Some of the individuals in the current population that have lower fitness are chosen as elite. These elite individuals are passed to the next population.
- Produces children from the parents. Children are produced either by making random changes to a single parent—mutation—or by combining the vector entries of a pair of parents—crossover.
- Replaces the current population with the children to form the next generation.
– The algorithm stops when one of the stopping criteria is met.
S: 1. OED – https://bit.ly/2Flzayx; https://bit.ly/2H3Kfqu (last access: 9 January 2019); WSG – http://www.wardsystems.com/ga101.asp (last access: 19 December 2018). 2 & 3. MathWorks – https://bit.ly/2EvULnJ; https://bit.ly/2rJDfEN (last access: 19 December 2018).
SYN: genetic-learning algorithm
S: TP – https://bit.ly/2Eu8iMt (last access: 19 December 2018)
CR: artificial intelligence, computational intelligence, computer science, intelligent system.
