Genetic algorithms are a type of evolutionary algorithm that use techniques inspired by Darwinian evolution, such as inheritance, mutation, selection, and crossover. The algorithm begins with a randomly generated population that is evaluated and selected to produce a new generation, undergoing this process until a solution is found. Key components include individuals representing possible solutions, a fitness function to evaluate solutions, and genetic operators like crossover and mutation that are applied to selected individuals to create new solutions for the next generation. Genetic algorithms have been successfully applied to optimization and search problems.

1. FUNDAMENTALS OF
GENETIC ALGORITHM
By
M. Sri Nandhini

2. Introduction
After scientist became disillusioned with classical and non-
classical attempts at modeling intelligence , they looked in
other directions.
Two prominent fields arose, connectionism (neural
networking, parallel processing) and evolutionary computing.
Basic concept- to stimulate process in natural system
necessary for evolution.

3. What is GA
A genetic algorithm (or GA) is a search technique used in
computing to find true or approximate solutions to
optimization and search problems.
Genetic algorithms are categorized as global search
heuristics.
Genetic algorithms are a particular class of evolutionary
algorithms that use techniques inspired by evolutionary
biology such as inheritance, mutation , selection and
crossover.

4. What is GA
The evolution usually starts from a population of
randomly generated individuals and happens in generations.
In each generation, the fitness of every individual in the
population is evaluated, multiple individuals are selected
from the current population and modified to form a new
population.
The new population is then used in the next iteration of
the algorithm.

5. Commonly ,the algorithm terminates when either a
maximum number of generations has been
produced, or a satisfactory fitness level has been
reached for the population.
If the algorithm has terminated due to a maximum
number of generations, a satisfactory solution may
or may not have been reached.
What is GA

6. Key Terms
Individual-Any possible solution.
Population-Group of all individuals.
Search Space-All possible solutions to the problem.
Chromosome-Blueprint for an individual.
Trait-Possible aspect(feature) of an individual.
Allele-Possible settings of trait(black , blond, etc.,).
Locus-The position of a gene on the chromosome.
Genome-Collection of all chromosomes for an
individual.

7. GA Requirements
A typical genetic algorithm requires two things to be
defined: a genetic representation of the solution domain
and a fitness function to evaluate the solution domain.
A standard representation of the solution is an array of
bits. Arrays of other types and structures can be used in
essentially the same way.
Tree like representations are explored in genetic
programming.

8. Basics of GA
The most common type of genetic algorithm works like
this: a population is created with a group of individuals
created randomly.
The individuals in the population are then evaluated.
The evaluation function is provided by the programmer
and gives the individuals a score based on how well they
perform at the given task.
Two individuals are then selected based on their fitness, the
higher the fitness, the higher the chance of being selected.

10. General Algorithm for GA
Reproduction
The next step is to generate a second generation
population of solutions from those selected through
genetic operators: crossover and mutation.
Termination
This generational process is repeated until a termination
condition has been reached.

12. Genetic Algorithm: History
Evolutionary computing-1960 by Rechenberg
Developed by John Holland , university of Michigan-1970.
Got popular in the late 1980’s.
Based on ideas from Darwinian Evolution theory
“Survival of the fittest”.
1986-Optimization of a Ten Member plane.

13. Basic Concept
GA converts design space into genetic space.
Works with a coding variables.
Traditional optimization techniques are deterministic in
nature, but GA uses randomized operators.
Three important aspects:
a) Definition of objective function.
b) Definition and implementation of genetic
representation.
c) Definition and implementation of genetic operators.

15. Biological Background
Each cell of a living organisms contains
chromosomes-strings of DNA.
Each chromosome contains a set of genes-blocks of
DNA.
A collection of genes-genotype.
A collection of aspects(like eye color)-phenotype.

16. Reproduction involves recombination of genes
from parents.
The fitness of an organism is how much it can
reproduce before it dies.
Biological Background

17. CONCLUSION:
There is no better algorithm than “Genetic Algorithm”. The
high efficiency of the algorithm allows not only the execution
of thousands of runs in minutes but also the undertaking of
non-trivial tasks with which to make the analysis

18. THANK YOU!