This presentation summarizes a project using a genetic algorithm to solve an unconstrained nonlinear programming problem. A genetic algorithm was used to optimize the function f(x) = x^2. It describes how genetic algorithms work by maintaining a population of candidate solutions and using variation operators like selection, crossover and mutation to evolve toward an optimal solution over generations. The presentation outlines the problem specification, solution method, source code, advantages like global optimization abilities, and disadvantages such as computational complexity.

1. MATHS
PRESENTATION
PRESENTED BY:-
GAURAV DEV
PIYUSH PAREWA
VAIBHAV
HARSH
LAVKUSH JATAV
DEPARTMENT OF ELECTRICAL ENGINEERING
IN THE GUIDANCE OF :-
Dr. PRIYANKA ASHOK HARJULE
[Ass.proffessor (mathematics)]

2. CONENT :-
1] Introduction
2] Problem specification
3] Solution of the problem
4] Source code
5] Advantages and disadvantages of
genetic algorithm
6] Conclusion

3. PROJECT DESCRIPTION
Computer program for solving a unconstrained
Nonlinear Programming problem through any of
the metaheuristic algorithm.

4. GENETIC ALGORITHM
• Genetic Algorithms are intelligent
search techniques maintaining a
population of candidate solutions
for a given problem and search the
solution space by applying
variation operators.

5. What is Genetic Algorithm?
•The genetic algorithm is a method for solving both
constrained and unconstrained optimization problems that
is based on natural selection, the process that drives
biological evolution. The genetic algorithm repeatedly
modifies a population of individual solutions. At each step,
the genetic algorithm selects individuals from the current
population to be parents and uses them to produce the
children for the next generation. Over successive
generations, the population "evolves" toward an optimal
solution.
•

6. PROBLEM SPECIFICATION:
OPTIMIZE THE NON LINEAR PROGRAMMING
PROBLEM USING GENETIC ALGORITHM .
F(x) = x^2

7. HOW GENETIC ALGORITHM WORKS ?

8. SOURCE CODE :

9. • .

10. OUTPUT :-

11. ADVANTAGES OF GENETIC ALGORITHM
• 1. EXPLORATION OF SEARCH SPACE
• 2. FLEXIBILITY
• 3. ADAPTABILITY
• 4.PARALLEL PROCESSING
• 5.GLOBAL OPTIMISATION

12. DISADVANTAGES OF GENETIC ALGORITHM
• 1. COMPUTATIONAL COMPLEXITY
• 2. DIFFICULTY IN TUNING PARAMETERS
• 3. DEPENDENCE ON RANDOMNESS
• 4. RISK OF PREMATURE CONVERGENCE
• 5. LIMITED UNDERSTANDING OF RESULTS

13. CONCLUSION :-
GENETIC ALGORITHMS ARE A VERSATILE
OPTIMIZATION TECHNIQUE THAT CAN FIND
SOLUTIONS TO A WIDE RANGE OF COMPLEX
PROBLEMS . THEY ARE PARTICULARLY USEFUL
WHEN OTHER OPTIMIZATION METHODS MAY
NOT BE SUITABLE OR WHEN AN ANALYTICAL
SOLUTION IS NOT READILY AVAILABLE.

14. THANK YOU