Genetic Algorithm

Genetic Algorithm (GA)

Genetic Algorithms are the heuristic search and optimization techniques that mimic the process of natural evolution.

Advantages/Benefits of Genetic Algorithm

  1. The concept is easy to understand.
  2. GA search from a population of points, not a single point.
  3. GA use payoff (objective function) information, not derivatives.
  4. GA supports multi-objective optimization.
  5. GA use probabilistic transition rules, not deterministic rules.
  6. GA is good for “noisy” environments.
  7. GA is robust w.r.t. to local minima/maxima.
  8. GA is easily parallelised.
  9. GA can operate on various representation.
  10. GA is stochastic.
  11. GA work well on mixed discrete/continuous problem.

Disadvantages of Genetic Algorithm

  1. GA implementation is still an art.
  2. GA requires less information about the problem, but designing an objective function and getting the representation and operators right can be difficult.
  3. GA is computationally expensive i.e. time-consuming.


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