not how we got there.
in the solution space and try to search the space in parallel.
from different locations to climb up the hill.
problem. You will soon find out that what we mean by interact and evolve.
hence the name Genetic Algorithms, commonly terms as GA.
time, generations become better and better adapted to survive.
improve the overall performance of the complete search space.
A very basic genetic algorithm can be stated as below.
the solution to a few problems through GA.
which steps correspond to what.
check various individuals/ solutions for being better than others in the population.
to get optimal solution given some parameters.
polynomial that best fits the given data.
Let us solve this problem in detail. Consider that the given points are as follows.
optimal solution given these number of iterations on this data.
Inheritance or Crossover, each "Individual" (or "solution") has two parents.
one individual to the tail of the other, as shown in the diagram below.
in general turns out to be a combination of both, i.e., crossover with mutation.
famous books in AI. Its called the Eight Queens Problem.
columns and eight rows as shown below.
We will use the representation as shown in the figure below.
6th row so on till in the 8th column the queen is in the 1st row.
board configuration is given in the diagram on the next page.
Let us quickly go though an example of how to solve this problem using GA.
selected individuals which are the fittest ones according to the fitness function.
Similarly, the mutation step can be done as under.
random position. Hence GA is totally a random technique.
One of the solutions to the problem is shown as under whose fitness value is 8.
The following flow chart summarizes the Genetic Algorithm.
as discussed in the chapter.