Benefits of using genetic algorithm Can anyone explain to me the benefits of the genetic algorithm compared to other traditional search and optimization methods?
 A: *

*Concept is easy to understand

*Modular, separate from application   

*Supports multi-objective    

*optimization Good for “noisy” environments    

*Always an answer; answer gets better with time    

*Inherently parallel; easily distributed

A: Genetic algorithms differ from traditional search and optimization methods in four significant points:


*

*Genetic algorithms search parallel from a population of points. Therefore, it has the ability to avoid being trapped in local optimal solution like traditional methods, which search from a single point.

*Genetic algorithms use probabilistic selection rules, not deterministic ones.

*Genetic algorithms work on the Chromosome, which is encoded version of potential solutions’ parameters, rather the parameters themselves.

*Genetic algorithms use fitness score, which is obtained from objective functions, without other derivative or auxiliary information

A: Genetic algorithms are kind of a last resort. They are useful only when an analytical solution is not feasible (see Patrick's answer for the most common reasons), and you have a lot of CPU time on your hands.
A: The main reasons to use a genetic algorithm are:


*

*there are multiple local optima

*the objective function is not smooth (so derivative methods can not be applied)

*the number of parameters is very large

*the objective function is noisy or stochastic


A large number of parameters can be a problem for derivative based methods when you don't have the definition of the gradient.  In this type of situation, you can find a not-terrible solution via GA and then improve on that with the derivative based method.  The definition of "large" is growing all the time.
A: Advantages of GAs compared to conventional methods:
 1. Parallelism, easily modified and adaptable to different problems
 2. Easily distributed
 3. Large and wide solution space search ability 
 4. Non-knowledge based optimisation process used of a fitness function
    for evaluation
 5. Easy to discover global optimum and avoid trapping in local optima
 6. Capable of multi-objective optimisation can return suite of
    potential solutions
 7. Good choice for large scale/wide variety of optimisation  problem
