Genetic Algorithms in Machine Learning
There is a need for a tool or a technique that reduces human dependency and decision-making should be done based on some algorithm or a mathematical formula. Genetic Algorithms (GA) are intrinsically parallel. Most other algorithms are serial and they can only explore the solution space to a problem in one direction at a time.
Genetic Algorithms have multiple offspring, they can explore solution space in multiple directions at once. If one path turns out to be a dead-end, they can easily eliminate it. GA performs well in problems for which the fitness land space is complex – ones where the fitness function is discontinuous, noisy, changes over time, or has many local optima.