Genetic algorithms are a heuristic search technique inspired by biological evolution to find optimized solutions to problems. The workflow involves initially generating a random population which is then evaluated based on a fitness function. Individuals are selected from the population based on their fitness for reproduction, with crossover and mutation occurring to create a new generation. This process is repeated until an optimal solution is found. Genetic algorithms have applications in fields like robotics, medicine, and computer gaming. They provide advantages like not requiring derivatives and being able to optimize both continuous and discrete functions, but also have limitations such as computational expense and not guaranteeing optimal solutions.

1. Genetic
Algorithm

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Team Members

3.  Introduction
 Definition
 Properties
 Applications
 Advantages
 Limitations
Overview

4. Genetic Algorithm (GA) is a
search-based optimization
technique based on the
principles of Genetics and
Natural Selection.
Optimization is the process
of making something
better.
Introduction

5. What
is
Genetic Algorithm?
A heuristic search technique used in computing and Artificial
Intelligence to find optimized solutions to search problems using
techniques inspired by evolutionary biology.
Genetic algorithms are commonly used to generate high-quality
solutions to optimization and search problems by relying on bio-
inspired operators such as selection, crossover & mutation.

6. WORKFLOW

7. INITIAL POPULATION
Initial Population
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8. FITNESS
FUNCTION
Initial Population Fitness Function
A good fitness function return
better state for the next
generation.
Fitness Score:
24+23+20+11 = 78
Probabilities Of Population
{ (24/78) x 100 } = 31%
{ (23/78) x 100 } = 29%
{ (20/78) x 100 } = 26%
{ (11/78) x 100 } = 14%
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23
20
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31%
29%
26%
14%

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SELECTION

10. Crossover
Foreachpairtobemated acrossover
pointis chosen.
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11. MUTATION
Each location in the bit string can besubject toa
mutation with small randomprobability

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MUTATION

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a)
Initial Population
b)
Fitness
Function
c)
Selection
d)
Cross-Over
e)
Mutation
HOW GENETIC ALGORITHM WORKS
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14. Applications of
Genetic Algorithm
Robotics: Path planning in robotic applications. Robotics
involves human designers and engineers trying out all sorts of
things in order to create useful machines that can do
work for humans.
Medical: Genetic Algorithms can be used throughout the
medical field. The GAs can help develop treatment programs,
optimize drug formulas, improve diagnostics. Plasma X-ray
Spectra Analysis: X-ray spectroscopic analysis is a powerful
tool for plasma diagnostics.
Computer Gaming: Those who spend some of their time playing computer
games (creating their own civilizations and
evolving them) will often find themselves playing
against sophisticated artificial intelligence the GAs
instead of against other human players online.
~ Crossover: In Pokemon tv series there was a chracter called pikachu.
Which evolved using crossover process with another character Ninja and the new
product from this crossover is Ninjachu.
~ Mutation : Red-Hair, Blue Eyes, Immunity, MCR1 – Pain tolerance,

15. ADVANTAGES
Does not require any derivative information (which
may not be available for many real-world problems).
Is faster and more efficient as compared to the
traditional methods.
Has very good parallel capabilities.
Optimizes both continuous and discrete functions and
also multi-objective problems.
Provides a list of “good” solutions and not just a single
solution.
.
.

16. LIMITATIONS
GAs are not suited for all problems,
especially problems which are simple and for
which derivative information is available.
Fitness value is calculated repeatedly which
might be computationally expensive for
some problems
Being stochastic, there are no guarantees on the
optimality or the quality of the solution.
If not implemented properly, the GA may not
converge to the optimal solution.
.
.

17. QUESTIONS?
?
.

18. THANK YOU