Genetic operators like selection, crossover and mutation are used in genetic algorithms to guide the algorithm towards a solution. Selection operators prefer better solutions and allow them to pass genes to the next generation. Crossover combines parent solutions into new solutions by recombining portions. Mutation encourages genetic diversity to prevent getting stuck in local minimums. These three operators work together for the algorithm to successfully find good solutions, with selection exploiting good solutions and mutation exploring to avoid getting trapped.

2.  A genetic operator is operator used in
genetic algorithms to guide the algorithm
towards a solution to a given problem.
 There are three main types of operators:
 Selection,
 Crossover,
 Mutation.

3.  Genetic operator are used to create and
maintain genetic diversity *(Mutation)
 Combine existing solution also known as
chromosomes
 Into a new solution*(Crossover)
 And select between solution*(selection)

4.  Mutation(or mutation-like)operators are said
to be *unary operators
 They only operate on one chromosome at a
time
 In contrast crossover operator are said to be
*binary operators
 As they operate on two chromosome into
existing chromosomes into one new
chromosomes

5.  Genetic variation is a necessity foe the
process of *evolution.
 Genetic operators used in genetic algorithm
are analogous to those in the natural world
 Survival of the selection ;
 Reproduction crossover also called
recombination and mutation.

6.  Selection operators give preference to better
solution(chromosomes)allowing them to
pass their “genes” to the next generation of
the algorithm
 The selection operator may also simply pass
the best solution from the current generation
directly to the next generation without being
mutated
 This is known as elitist selection.

7.  Cross over is the process of taking more
than one parent solution and producing child
solution from them.
 By recombine portion of good solution the
genetic algorithm is more likely to create a
better solution

8.  There are a number of different methods for
combining the parent solution including the
edge recombination operator(ERO) and the
‘cut and splice and uniform crossover
method’.
 Types of crossover:
 Single point crossover,
 Two point crossover,
 Uniform crossover.

9.  The mutation operator encourages genetic
diversity among solution and attempts to
prevent the genetic algorithm .
 Converging to a local minimum by stopping
the solution becoming too close to one
another.

10.  There are different methods of mutation may be
used, these are range from,
 Bit mutation,
 Flip bit mutation,
 Boundary mutation,
 Non uniform mutation,
 Uniform mutation,
 Gaussian mutation,
 shrink mutation.

11.  The operators must work in conjunction with
each other for the algorithm to be successful
in finding a good solution.
 Selection operator on its own will tend to fill
the solution population with copies of the
best solution from population.
 If the selection and crossover operators are
used without the mutation operator the
algorithm will tends to converge to a local
minimum.

12.  Using mutation operator on its own leads to
a random walk through the search space.
 Only by using all the three operators
together can genetic algorithm become a
“noise-tolerant hill-climbing algorithm”
 Yielding good solution to the problem...

13. THANK YOU....