Genetic Algorithms techniques in solving a searching problem for optimization.
-Before we jump into explaining the details, we need to understand that such a problem could be solved using dynamic programming or any other common algorithm, this is just a simple example of applying genetic algorithms ,the point is, it is useful when the search space becomes explosive as complexity of dynamic programming is O(n^2) .
-Sample of the output, to compare my results that are supposed to be trying to reach the best solution (Optimization) and the usage of an online tool that actually gets the best solution:
-So, as seen above genetic algorithms that is based on GUIDED Randomness and Probability could reach a profit of 3794 ,where the profit reached optimally by dynamic programming is 3899 .