Genetic Algorithm

'Genetic algorithms operate on a set of possible solutions. Because of the random nature of genetic algorithms, solutions found by an algorithm can be good, poor, or infeasible [defective, erroneous], so there should be a way to specify how good that solution is. This is done by assigning a fitness value [or just fitness] to the solution. Chromosomes represent solutions within the genetic algorithm. The two basic components of chromosomes are the coded solution and its fitness value.' to find out more...

Codes from Matlab:
https://www.mathworks.com/matlabcentral/fileexchange/14767-genetic-algorithm

Online findings:

• https://ibug.doc.ic.ac.uk/media/uploads/documents/courses/GeneticAlgorithm-tutorial.pdf
• http://mason.gmu.edu/~jgero/publications/1998/98JoGeroAIEDAM.pdf
• https://www.sciencedirect.com/science/article/pii/S2095263512000490
• https://www.whitman.edu/Documents/Academics/Mathematics/2014/carrjk.pdf