Genetic algorithm

Introduction to Genetic
Algorithm (GA)
Presented By: Rabiya Khalid
Student of MS(CS)
Research Domain: Energy management in Smart Grid
Supervised By: Dr. Nadeem Javaid

GA (1/30)
• Introduction [1]
– Based on Darwin’s theory of evolution
– Used to find true or approximate solution
– Solves optimization based problems
– Techniques inspired by evolutionary biology
o Inheritance
o Mutation
o Selection
o Crossover
• History
– Evolutionary computing evolved in the 1960’s by I.Rechenberg
– GA’s were invented by John Holland in the mid-70’s.
2
[1]: Eiben, Agoston E., P-E. Raue, and Zs Ruttkay. "Genetic algorithms with multi-parent recombination."
In International Conference on Parallel Problem Solving from Nature, pp. 78-87. Springer Berlin
Heidelberg, 1994

GA (2/30)
3
[2]
[2]: Boultif, Ayache, Azeddine Kabouche, and Segni Ladjel. "Application of Genetic Algorithms (GA) and
Threshold Acceptance (TA) to a Ternary Liquid-Liquid Equilibrium System." International Review on
Modelling and Simulations (IREMOS) 9, no. 1 (2016): 29-36.

• Lets learn biology first [3]
– 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
• 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.
GA (3/30)
4
[3]: http://www.yourgenome.org/facts/what-is-evolution (accessed:1Feb2017)

• Natural selection [4]
– 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 [5]
– A genetic algorithm maintains
o Population of candidate solutions for the problem
o Makes it evolve by iteratively applying a set of stochastic operators
GA (4/30)
5[4]: https://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol1/hmw/article1.html (accessed:1Feb2017)
[5]: https://en.wikipedia.org/wiki/Genetic_algorithm (accesed:1Feb2017)

GA (5/30)
6
[6]
[6]: http://mainroach.blogspot.com/2013/07/css-compression-genetic-minification.html
(accessed:1Feb2017)

• Key terms [7]
– 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
GA (6/30)
7
[7]: http://geneticalgorithms.ai-depot.com/Tutorial/Overview.html (accessed:1Feb2017)

• 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 [8]
– Chromosomes could be
• 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 ...
GA (7/30)
8[8]: http://www.obitko.com/tutorials/genetic-algorithms/ga-basic-description.php
(accessed:1Feb2017)

• Algorithm
1. Initialization
2. Generate population
3. Evaluate fitness
4. While gen<max_gen do
5. Select two parents
6. Crossover
7. Mutation
8. Evaluate fitness
9. Elitism
10. End
GA (8/30)
9

• Fitness function [7]
– 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
GA (9/30)
10

• Selection [7]
– 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
GA (10/30)
11
[9]

• Crossover [7]
– Single point crossover
– Two point crossover
– Uniform crossover
GA (11/30)
12

GA (12/30)
• Mutation [7]
– 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
• Types of mutation
– Bit inversion
o Selected bits are inverted
– Order changing
o The number are selected and
exchanged
13

• 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
GA (13/30)
14

• Advantages of GA [10]
– 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 [11]
– 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
GA (14/30)
15
[10]: Kim, Dong Hwa, Ajith Abraham, and Jae Hoon Cho. "A hybrid genetic algorithm and bacterial
foraging approach for global optimization." Information Sciences 177, no. 18 (2007): 3918-3937.
[11]: Morris, Garrett M., David S. Goodsell, Robert S. Halliday, Ruth Huey, William E. Hart, Richard K.
Belew, and Arthur J. Olson. "Automated docking using a Lamarckian genetic algorithm and an empirical
binding free energy function." Journal of computational chemistry 19, no. 14 (1998): 1639-1662

GA (15/30)
16
[12]
[12]: https://www.youtube.com/watch?v=ejxfTy4lI6I (accessed:2Feb2017)

• Appliances
• Home appliances are divided into
3 types
– Fixed appliances
– Shiftable appliances
– Elastic appliances
• This categorization is based on
– Power consumption pattern
– Time of use
– User comfort
32
Appliances Power
rating
Fan 0.6
Lights 0.75
Clothes iron 1.5
Microwave
oven
1.18
Toaster 0.5
Coffee
maker
0.8
[13]: Exploiting heuristic algorithms to efficiently utilize energy management controllers with renewable
energy sources", Energy and Buildings, Volume 129, 1 October 2016, Pages 452-470, ISSN 0378-7788,
Energy Optimization by GA (1/15) [13]

1. Fixed Appliances
– Regular appliances (basic
load)
– LOT cannot be modified
2. Shiftable appliances
– Can be shifted without
altering their load profile
– User specifies an operational
time interval for these
appliances
3. Elastic appliances
– Interruptible appliances
– Load profile can be modified
– Can be shifted to a suitable
time slot
33
Appliances Power
rating
Start
time
End
time
AC 1.44 6 24
refrigerator 0.73 6 24
Water
heater
4.45 6 24
Space
heater
1.50 6 24
Appliances Power
rating
Start
time
End
time
Waiting
time
Washing
machine
0.78 3 16 5
Dish washer 3.60 3 12 5
Cloth dryer 4.40 3 18 5

• Pricing schemes
• TOU pricing scheme [14]
– Prices set for on peak and off peak
hours
– Hours are divided into blocks
– Each block has fixed price
• IBR pricing scheme [15]
– Prices are divided into several
blocks
– First block is at the lowest
electricity price
– With the increment in electricity
consumption price also increased
34
0
2
4
6
8
10
12
14
16
18
20
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
TOU
[14]: Waterloo North Hydro, https://www.wnhydro.com/en/your-
home/time-of-use-rates.asp. Last visited: 20 May 2016.
[15]: http://www.prepayment.eskom.co.za/IBT.asp

35
• Pricing schemes
• Real time Pricing scheme (RTP) [14]
• Rates are based on hourly
consumption of electricity
• Utility regulates RTP into two parts
1. Base bill: It depends on customer’s
base load (CBL)
2. Hourly prices: Hourly prices are
applied according to customer’s
usage (difference between actual
and CBL)
0
5
10
15
20
25
30
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
RTP
Price
Unit
if Load<=Limit
Price
Unit
+Extra changes if Load>Limit
RTP =

36
• Pricing schemes
• Variable Peak Pricing (VPP) [14]
• Critical Peak Pricing(CPP) [14]
• During power system emergency
conditions
• Usually occurs in hot summer week
days
• CPR [14]
• During critical events utility
increases price
• Refunds customer when demand
decreases
0
20
40
60
80
100
120
140
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
CPP

• GA parametric list
• Input parameters for GA
• These inputs are problem dependent
• Values of these parameters can be changed to tune the
algorithm
• Following table shows values of parameters for our problem
37
Parameters Values
Population size 200
Selection Random selection
Crossover 0.8%
Mutation 0.2%
Stopping criteria Max.generation
Max.generation 800

38
GA parameters General use HEM’s parameters
Individual Any possible solution On-off status of appliances
in a time slot
Population Group of all individuals Collection of all individuals
Search Space All possible solutions to the
problem
Any values in constraint
defined limits
Chromosome Blueprint for an individual Time slot of an hour
Allele Possible settings of trait
(black, blond, etc.)
Characteristics of defined
system to satisfy
optimization limits e.g 0/1
Locus The position of a gene on
the chromosome
Location of an appliance
Genome Collection of all
chromosomes for an
individual
Binary on /off (1/ 0) states
arrays for each appliance

• Generate population
39
Washing
machine
Dish
washer
Cloth
dryer
Air
conditioner
Water
heater
1 0 0 1 0
0 1 1 1 1
1 1 0 0 1
1 0 0 1 1
1 1 1 0 0
0 1 0 1 1
1 1 0 1 0
for j=1:popsize
for i=1:tbits
if rand(1)>=0.1
X=1;
else
X=0;
end
pop(j,i)=X;
end
c=zeros(1,10);
if pop(j,:)==c
pop(j,:)= ones(1,10);
end
end

• Evaluate Fitness
40
Washing
machine
Dish
washer
Cloth
dryer
Air
conditioner
Water
heater
Fitness
1 0 0 1 0 5.592
0 1 1 1 1 7.92
1 1 0 0 1 8.53
1 0 0 1 1 10.5
1 1 1 0 0 5.026
0 1 0 1 1 8.259
1 1 0 1 0 6.842
for t=1:24
for a=1:popsize
F(a,1)=electricity_cost*x(a,:)';
err=c_electricity_cost*x(a,:)'-d;
F(a,1)=F(a,1)+1000*abs(err);
end
end

• Select best solution
41
Washing
machine
Dish
washer
Cloth
dryer
Air
conditioner
Water
heater
Fitness
1 0 0 1 0 5.592
0 1 1 1 1 7.92
1 1 0 0 1 8.53
1 0 0 1 1 10.5
1 1 1 0 0 5.026
0 1 0 1 1 8.259
1 1 0 1 0 6.842
Best
solution

• Crossover
cpoint=floor(nn*rand);
if cpoint==0
cpoint=randi(10,1,1);
end
c=[a(1:cpoint) b(cpoint+1:end)];
d=[b(1:cpoint) a(cpoint+1:end)];
42
Washing
machine
Dish
washer
Cloth
dryer
Air
conditioner
Water
heater
1 0 1 0 0
0 1 0 1 0
1 1 1 1 1
1 0 0 0 0
1 1 1 0 0
0 1 1 0 0
1 1 1 1 1
New Population after crossover

• Mutation
j=floor(rand*nn);
k=a(j);
if k==1
a(j)=0;
else
if k==0
a(j)=1;
end
end
43
Washing
machine
Dish
washer
Cloth
dryer
Air
conditioner
Water
heater
1 0 1 0 0
0 0 0 1 0
1 1 0 1 1
1 0 0 1 0
1 1 1 1 0
0 1 0 0 0
1 1 0 1 1
New Population after mutation

• Comparison
– Check if all appliances are in off state in any individual
– Compare new population with appliances’ data
• Whether an appliance has finished it LOT
• What is the remaining waiting time of an appliance
for a=1:popsize
for bs=1:tbit
if time_slots(1,b)==0
popnew(a,b)=0;
end
if lgen<=time_slots(1,b)
popnew(a,b)=1;
end
end
z=zeros(1,10);
o=ones(1,10);
if popnew(a,:)=0;
popnew(a,:)=o;
end
end
44

• Evaluate Fitness of updated population
45
Washing
machine
Dish
washer
Cloth
dryer
Air
conditioner
Water
heater
Fitness
1 0 1 0 0 5.592
0 0 0 1 0 7.92
1 1 0 1 1 8.53
1 0 0 1 0 10.5
1 1 1 1 0 9.026
0 1 0 0 0 8.259
1 1 0 1 1 6.842
Best
solution

• Elitism
– After evaluating the new population
• New best solution is compared with previous best solution
• If fitness of new best solution is better
– It is copied as global best
– Else global best solution remains unchanged
if gbest>best
gbest=best;
end
– Algorithm will terminate when termination criteria
will be satisfied
46

Genetic algorithm

More Related Content

What's hot (20)

Viewers also liked (20)

Similar to Genetic algorithm (20)

Recently uploaded (20)

Genetic algorithm

Editor's Notes