This document provides an introduction to genetic algorithms. It defines genetic algorithms as optimization techniques that are inspired by natural selection and genetics. Genetic algorithms are frequently used to find optimal or near-optimal solutions to complex problems. The document outlines the basic terminology used in genetic algorithms, including populations, chromosomes, genes, alleles, phenotypes, and genetic operations like crossover and mutation. It also describes the basic structure of a genetic algorithm, which involves initializing a population, calculating fitness, performing genetic operations, selecting survivors, and iterating until a termination condition is reached.

1. GENETIC ALGORITHM
Submitted by,
M. Dharani., B.Sc.,
Nadar Saraswathi college of Arts &
Science,
Theni.

2. INTRODUCTION
Genetic algorithm is a search based
optimization technique based on the
principles of genetics and natural
selection.
It is frequently used to find optimal
or near optimal solutions to difficult
problems which otherwise would
take a lifetime to solve.

3. INTRODUCTION TO
OPTIMIZATION
Optimization is the process of
making something better.
process
Set of
inputs
Set of
outputs

4. BASIC TERMINOLOGY
 POPULATION: It is a subset of all the
possible(encoded) solutions to the given
problem.
 CHROMOSOMES: A chromosome is one
such solution to the given problem.
 Gene: A gene is one element position of a
chromosome.
 ALLELE: It is the value a gene takes for
a particular chromosome.

5. PHENOTYPE:
Phenotype is the population in
the actual real world solution space
in which solutions are represented
in a way they are represented in
real world situations.
ENCODING AND DECODING:
For simple the phenotype and
genotype space are the same.

6. GENETIC OPERATION:
These alter the genetic
compositions of the offspring's.
These include crossover, mutation,
selection, etc.
FITNESS FUNCTION:
A fitness function is a
function which takes the solution as
inputs and produces the suitability
of the solution as the output.

7. BASIC STRUCTURE OF
GENETIC ALGORITHM
Population
initialization
Fitness
function
calculation
Cross over
Mutation
Survivor
selection
Terminates and
return best
Loop until
termination
critic
reached

8. GENOTYPE
REPRESENTATION
BINARY REPESENTATION
REAL VALUED REPRESENTATION
0 0 1 0 1 1 1 0 0 1
0.5 0.2 0.6 0.8 0.7 0.4 0.3 0.2 0.1 0.9

9. GA POPULATION
Population is a subset of solutions
in the current generation.
Set of Chromosomes.
POPULATION INITIALIZATION :
A. Random Initialization
B. Heuristic Initialization
POPUATION MODEL:
A. Steady state
B. Generational

10. FITNESS FUNCTION
Takes a candidate solution
to the problem as input and
produces as output.
The objectives is to either
maximize or minimize the
given objective function.

11. GA—PARENT SELECTION
CHROMOSOME FITNESS VALUE
A 8.2
B 3.2
C 1.4
D 1.2
E 4.2
F 0.3

12. GA--MUTATION
 Used to maintain and introduce diversity
in the genetic population.
 Mutation Operations:
 Bit Flip Mutation
0 0 1 1 0 1 0 0 1 0

13. GA—TERMINATION
CONDITION
When there has been no
improvement in the population for X
iterations.
When we reach an absolute number
of generations.
When the objective function value
has reached a certain pre-defined
value.

14. THANK YOU!!!