Genetic Algorithm (GA)
Genetic Algorithms are the heuristic search and optimization techniques that mimic the process of natural evolution.
Advantages/Benefits of Genetic Algorithm
- The concept is easy to understand.
- GA search from a population of points, not a single point.
- GA use payoff (objective function) information, not derivatives.
- GA supports multi-objective optimization.
- GA use probabilistic transition rules, not deterministic rules.
- GA is good for “noisy” environments.
- GA is robust w.r.t. to local minima/maxima.
- GA is easily parallelised.
- GA can operate on various representation.
- GA is stochastic.
- GA work well on mixed discrete/continuous problem.
Disadvantages of Genetic Algorithm
- GA implementation is still an art.
- GA requires less information about the problem, but designing an objective function and getting the representation and operators right can be difficult.
- GA is computationally expensive i.e. time-consuming.