2. M d . S h a f a y a t u l A h s a n
I D : 1 4 2 0 0 0 4 1 2
M d . M u b a s s h i r R a i h a n
I D : 1 4 2 0 0 0 7 1 2
M d . E f t h a k h a r U l A l a m
I D : 1 4 2 0 0 0 5 1 2
Team Members
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.
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%
24748552
32752411
24415124
32543213
24
23
20
11
31%
29%
26%
14%
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.
.
.
