a) image processing	e) criminal suspect recognition
b) scheduling	f) job shop optimization
c) stock selection	g) forex trading rules
d) horse-race betting	h) etc

 The actual step of this algorithm are :

  a) An initial population of random solution is created.
  b) A fitness value is then assigned to each individual based on its evaluation to the current problem.
  c) Individuals or chromosomes with a higher fitness value are selected to parent new solutions during reproduction.
  d) The new offspring are then set to replace the old (low fitness values). Thus a generation is completed.
  e) Return to step (b) through (d) until the population converged.
 

  Product Features :

    xl bit  is _

• User-friendly and no programming needed. (see screenshot)

• Very similar to Excel Solver set up.

• Maximum 500 population allowed.

• 3 choices of crossover methods. (see screenshot)

• Adjustable crossover and mutation rates.

• Unlimited variables in one optimization problem. (Depend on user computer speed and memory)

• Two types of fitness scaling. (see screenshot)

• Multiple fitness functions.

• 4 type of variables i.e. binary, integer, non-integer and permutation.

• Adjustable random number seed so that user can use same set of initial population with different GA parameters.

• Example files provided.

• Full report and fitness graph. (see screenshot)

• see variables changes on the fly. (see screenshot)

• adjust crossover and mutation rate on the fly.

• Top six  solutions are shown in report.