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Introduction to Genetic
Algorithm (GA)
Presented By: Rabiya Khalid
Department of Computer Science
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• Introduction
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Based on Darwin’s theory of evolution
Rapidly growing area of artificial intelligence
Used to solve optimization based problems
Techniques inspired by evolutionary biology
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Inheritance
Mutation
Selection
Crossover
• History
– Evolutionary computing evolved in the 1960’s by I.Rechenberg
– GA’s were invented by John Holland in the mid-70’s.
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• Lets learn biology first
– Our body is made up of trillions of cells
– Each cell has a core structure (nucleus)
that contains chromosomes
– Each chromosome is made up of tightly
coiled strands of deoxyribonucleic acid
(DNA)
– Genes are segments of DNA that
determine specific traits, such as
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Eye color
Hair color
– A gene mutation is an alteration in DNA
– It can be inherited or acquired during
your lifetime
– Some changes in genes result in genetic
disorders.
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• Natural selection
– Only the organisms best adapted to their environment tend to
survive
– Transmit their genetic characteristics in increasing numbers to
succeeding generations
– Those less adapted tend to be eliminated
• GA is inspired from nature
– A genetic algorithm maintains
o Population of candidate solutions for the problem
o Makes it evolve by iteratively applying a set of stochastic operators
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• Key terms
– Individual
o Any possible solution
– Population
o Group of all individuals
– Search Space
o All possible solutions to the problem
– Chromosome
o Blueprint for an individual
– Trait
o Possible aspect (features) of an
individual
– Allele
o Possible settings of trait (black, blond,
etc.)
– Locus
o The position of a gene on the
chromosome
– Genome
o Collection of all chromosomes for an
individual
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Key terms
General use
Role in Smart grid
Individual
Any possible solution
On-off status of appliances
in a time slot
Population
Group of all individuals
Collection of all
chromosomes
Search Space
All possible solutions to the Any values in constraint
problem
defined limits
Chromosome
Blueprint for an individual
Duplicate values of
objective function
Allele
Possible settings of trait
(black, blond, etc.)
Characteristics of defined
system to satisfy
optimization limits
Locus
The position of a gene on
the chromosome
Single bit (0 or 1)
Genome
Collection of all
chromosomes for an
individual
Binary on /off (1/ 0) states
arrays for each appliance 8
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• GA requirements
– A genetic representation of the solution domain (set search
space)
– A fitness function to evaluate the solution domain (objective
function according to system model in case of smart grid)
• Representation
– Chromosomes could be
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Bit strings
(0101 ... 1100)
Real numbers
(43.2 -33.1 ... 0.0 89.2)
Permutations of element (E11 E3 E7 ... E1 E15)
Lists of rules
(R1 R2 R3 ... R22 R23)
Program elements
(genetic programming)
... any data structure ...
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Algorithm
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Initialization
Generate population
Evaluate fitness
While gen<max_gen do
Select two parents
Crossover
Mutation
Evaluate fitness
Elitism
End
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• Fitness function
– The fitness function is always problem dependent
– Assigns fitness value to the individual
– It summarizes, how close a solution is to achieving the set aims
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• Selection
– Darwinian survival of fittest
– More preference to get better guys
– Ways to select
• Roulette wheel
• Tournament
• Truncation
– By itself, pick best
• Roulette wheel
– Probability of selection is assigned to individuals based on their
fitness value
– Two parents are selected randomly from set of individuals with high
selection probability
– Selected parents are used to produce off-springs
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• Crossover
– Single point crossover
– Two point crossover
– Uniform crossover
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• Mutation
– String subjected to mutation
after crossover
– It prevents falling all solutions
into local optimum
– It alters bits of chromosomes
to make the string better
– Bit wise mutation is
performed with probability of
mutation Po
– A random number ro is
generated and compared
with Po
– If Po>ro , then mutation
occurs
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• Types of mutation
– Bit inversion
o Selected bits are inverted
– Order changing
o The number are selected and
exchanged
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• Elitism
– Elitism involves copying a small proportion of the fittest
candidates, unchanged, into the next generation
– EA does not waste time re-discovering previously discarded
partial solutions
• Search space
– The set of solutions among which the desired solution resides
– Best solution among a number of possible solutions
– Represented by one point in the search space
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• Advantages of GA
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Concepts are easy to understand
Always an answer; answer gets better with time
Less time required for some special applications
Chances of getting optimal solution are more
• Limitations
– The population considered for the evolution should be
moderate or suitable
– One for the problem (normally 20-30 or 50-100)
– Crossover rate should be 80%-95%
– Mutation rate should be low i.e. 0.5%-1% assumed as best
– The method of selection should be appropriate
– Writing of fitness function must be accurate
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