The GA algorithm is inspired by the theory of biological evolution proposed by Charles Darwi. In fact, the main mechanism simulated in GA is the survival of the fittest. In nature, fitter organisms have a higher probability of survival. This assists them to transfer their genes to the next generation. Over time, good genes that allow species to better adapt with environment (avoid enemies and find foods) become dominant in subsequent generations. Inspired by chromosomes and genes in nature, the GA algorithm represents optimization problems as a set of variables. Each solution for an optimization problem corresponds to a chromosome and each gene corresponds to a variable of the problem.
Evolutionary computation is a family of algorithms for global optimization inspired by biological evolution, and the subfield of artificial intelligence and soft computing studying these algorithms. Technically speaking, they are a family of population-based trial and error problem solvers with a metaheuristic or stochastic optimization character..
My implementation of binary and discrete Genetic Algorithm.
My implementation of continious Genetic Algorithm.
Application of GA in binary image reconstruction problems.