Genetic Algorithms. 2 Overview Introduction To Genetic Algorithms (GAs) GA Operators and Parameters...
-
Upload
elaine-strickland -
Category
Documents
-
view
223 -
download
0
Transcript of Genetic Algorithms. 2 Overview Introduction To Genetic Algorithms (GAs) GA Operators and Parameters...
![Page 1: Genetic Algorithms. 2 Overview Introduction To Genetic Algorithms (GAs) GA Operators and Parameters Genetic Algorithms To Solve The Traveling Salesman.](https://reader034.fdocuments.in/reader034/viewer/2022051316/5697bf7a1a28abf838c82b25/html5/thumbnails/1.jpg)
Genetic Algorithms
![Page 2: Genetic Algorithms. 2 Overview Introduction To Genetic Algorithms (GAs) GA Operators and Parameters Genetic Algorithms To Solve The Traveling Salesman.](https://reader034.fdocuments.in/reader034/viewer/2022051316/5697bf7a1a28abf838c82b25/html5/thumbnails/2.jpg)
3
Introduction To Genetic Algorithms
(GAs)
![Page 3: Genetic Algorithms. 2 Overview Introduction To Genetic Algorithms (GAs) GA Operators and Parameters Genetic Algorithms To Solve The Traveling Salesman.](https://reader034.fdocuments.in/reader034/viewer/2022051316/5697bf7a1a28abf838c82b25/html5/thumbnails/3.jpg)
4
What Are Genetic Algorithms (GAs)?
Genetic Algorithms are search and optimization techniques based on Darwin’s Principle of Natural Selection.
![Page 4: Genetic Algorithms. 2 Overview Introduction To Genetic Algorithms (GAs) GA Operators and Parameters Genetic Algorithms To Solve The Traveling Salesman.](https://reader034.fdocuments.in/reader034/viewer/2022051316/5697bf7a1a28abf838c82b25/html5/thumbnails/4.jpg)
How is it different from other optimization and search procedures?
1. Works with a coding of the parameter set, not the parameters themselves
2. Search for a population of point and not a single point
3. Use objective function information and not derivatives or other auxiliary knowledge
4. Uses probabilistic transition rules and not deterministic rules
![Page 5: Genetic Algorithms. 2 Overview Introduction To Genetic Algorithms (GAs) GA Operators and Parameters Genetic Algorithms To Solve The Traveling Salesman.](https://reader034.fdocuments.in/reader034/viewer/2022051316/5697bf7a1a28abf838c82b25/html5/thumbnails/5.jpg)
How GA is used and different from other optimization techniques?
The first step in GA is to code the parameter x as a finite length string
Example 1 can be code as string of 5 bits with an output f=f(s) , where s=string of bits
Successive populations are generated using the GA
For effective check GA requires only objective functions associated with individual strings
![Page 6: Genetic Algorithms. 2 Overview Introduction To Genetic Algorithms (GAs) GA Operators and Parameters Genetic Algorithms To Solve The Traveling Salesman.](https://reader034.fdocuments.in/reader034/viewer/2022051316/5697bf7a1a28abf838c82b25/html5/thumbnails/6.jpg)
Simple genetic algorithm Reproduction:- individual strings are copied
according to their objective fn: values f(FITNESS FUNCTION)
Crossover:- Members of the newly reproduced strings are mated at random. Each pair of strings undergoes crossing overs.
Mutation:-supplements reproduction and crossover and acts as an insurance policy against premature loss of important notions
![Page 7: Genetic Algorithms. 2 Overview Introduction To Genetic Algorithms (GAs) GA Operators and Parameters Genetic Algorithms To Solve The Traveling Salesman.](https://reader034.fdocuments.in/reader034/viewer/2022051316/5697bf7a1a28abf838c82b25/html5/thumbnails/7.jpg)
8
Darwin’s Principle Of Natural Selection I
IF there are organisms that reproduce, and IF offsprings inherit traits from their parents, and IF there is variability of traits, and IF the environment cannot support all members of a
growing population, THEN those members of the population with less-
adaptive traits (determined by the environment) will die out, and
THEN those members with more-adaptive traits (determined by the environment) will thrive
The result is the evolution of species.
![Page 8: Genetic Algorithms. 2 Overview Introduction To Genetic Algorithms (GAs) GA Operators and Parameters Genetic Algorithms To Solve The Traveling Salesman.](https://reader034.fdocuments.in/reader034/viewer/2022051316/5697bf7a1a28abf838c82b25/html5/thumbnails/8.jpg)
9
Basic Idea Of Principle Of Natural Selection
“Select The Best, Discard The Rest”
![Page 9: Genetic Algorithms. 2 Overview Introduction To Genetic Algorithms (GAs) GA Operators and Parameters Genetic Algorithms To Solve The Traveling Salesman.](https://reader034.fdocuments.in/reader034/viewer/2022051316/5697bf7a1a28abf838c82b25/html5/thumbnails/9.jpg)
Example 1Maximize f(x) =x2 on the integer scale from 0-31
0 31x
f(x)
1000
![Page 10: Genetic Algorithms. 2 Overview Introduction To Genetic Algorithms (GAs) GA Operators and Parameters Genetic Algorithms To Solve The Traveling Salesman.](https://reader034.fdocuments.in/reader034/viewer/2022051316/5697bf7a1a28abf838c82b25/html5/thumbnails/10.jpg)
Example 1
No:
String Fitness % of total
1 01101
169 14.2
2 11000
576 49.2
3 01000
64 5.5
4 10011
361 30.9
Total 1170 100
![Page 11: Genetic Algorithms. 2 Overview Introduction To Genetic Algorithms (GAs) GA Operators and Parameters Genetic Algorithms To Solve The Traveling Salesman.](https://reader034.fdocuments.in/reader034/viewer/2022051316/5697bf7a1a28abf838c82b25/html5/thumbnails/11.jpg)
Roulette wheel with slots sized according to fitness
1234
![Page 12: Genetic Algorithms. 2 Overview Introduction To Genetic Algorithms (GAs) GA Operators and Parameters Genetic Algorithms To Solve The Traveling Salesman.](https://reader034.fdocuments.in/reader034/viewer/2022051316/5697bf7a1a28abf838c82b25/html5/thumbnails/12.jpg)
Crossover
A1=0 1 1 0 1A2=1 1 0 0 0
A1’=0 1 1 0 0A2’=1 1 0 0 1
![Page 13: Genetic Algorithms. 2 Overview Introduction To Genetic Algorithms (GAs) GA Operators and Parameters Genetic Algorithms To Solve The Traveling Salesman.](https://reader034.fdocuments.in/reader034/viewer/2022051316/5697bf7a1a28abf838c82b25/html5/thumbnails/13.jpg)
Simple GA by Hand(Reproduction)
No: String x f(x)x2
pselectfi/Ʃf
Expected countn.pselect
Actual count(Roulette Wheel0
1 01101 13 169 .14 .58 1
2 11000 24 576 .49 1.97 2
3 01000 08 64 .06 0.24 0
4 10011 19 361 0.31 1.24 1
Sum 1170 1.00 4 4.0
Average 293 0.25 1.00 1.0
Maximum 576 .49 1.97 2
![Page 14: Genetic Algorithms. 2 Overview Introduction To Genetic Algorithms (GAs) GA Operators and Parameters Genetic Algorithms To Solve The Traveling Salesman.](https://reader034.fdocuments.in/reader034/viewer/2022051316/5697bf7a1a28abf838c82b25/html5/thumbnails/14.jpg)
CrossoverMating Pool after Reproduction(Cross Site shown)
Mate Crossover
New population
x F(x)
0110|12 4 0 1 1 0 0 12 144
1100|01 4 1 1 0 0 1 25 625
11|0004 2 1 1 0 1 1 27 729
10|0113 2 1 0 0 0 0 16 256
Sum 1754
Average 439
Maximum 729
Probability of mutation in this test is 0.001. With 20 transferred bit positions we should expect 20*0.001=0.02
![Page 15: Genetic Algorithms. 2 Overview Introduction To Genetic Algorithms (GAs) GA Operators and Parameters Genetic Algorithms To Solve The Traveling Salesman.](https://reader034.fdocuments.in/reader034/viewer/2022051316/5697bf7a1a28abf838c82b25/html5/thumbnails/15.jpg)
Seeking similarities among strings in population
Causal relationships between similarities and high fitness
How does the directed search guide for improvement?
![Page 16: Genetic Algorithms. 2 Overview Introduction To Genetic Algorithms (GAs) GA Operators and Parameters Genetic Algorithms To Solve The Traveling Salesman.](https://reader034.fdocuments.in/reader034/viewer/2022051316/5697bf7a1a28abf838c82b25/html5/thumbnails/16.jpg)
17
Evolution in the real world Each cell of a living thing contains chromosomes -
strings of DNA Each chromosome contains a set of genes - blocks of DNA Each gene determines some aspect of the organism (like
eye colour) A collection of genes is sometimes called a genotype A collection of aspects (like eye colour) is sometimes
called a phenotype Reproduction involves recombination of genes from
parents and then small amounts of mutation (errors) in copying
The fitness of an organism is how much it can reproduce before it dies
Evolution based on “survival of the fittest”
![Page 17: Genetic Algorithms. 2 Overview Introduction To Genetic Algorithms (GAs) GA Operators and Parameters Genetic Algorithms To Solve The Traveling Salesman.](https://reader034.fdocuments.in/reader034/viewer/2022051316/5697bf7a1a28abf838c82b25/html5/thumbnails/17.jpg)
Basic Idea Of Principle Of Natural Selection
“Select The Best, Discard The Rest”
![Page 18: Genetic Algorithms. 2 Overview Introduction To Genetic Algorithms (GAs) GA Operators and Parameters Genetic Algorithms To Solve The Traveling Salesman.](https://reader034.fdocuments.in/reader034/viewer/2022051316/5697bf7a1a28abf838c82b25/html5/thumbnails/18.jpg)
AlgorithmGenerate Initial Populationdo Calculate the Fitness of each member do { Select Parents from current population Perform Crossover add offspring to the new population Merge new population into the current population Mutate current population till result
is obtained }
![Page 19: Genetic Algorithms. 2 Overview Introduction To Genetic Algorithms (GAs) GA Operators and Parameters Genetic Algorithms To Solve The Traveling Salesman.](https://reader034.fdocuments.in/reader034/viewer/2022051316/5697bf7a1a28abf838c82b25/html5/thumbnails/19.jpg)
Population
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) ... any data structure ...
population
![Page 20: Genetic Algorithms. 2 Overview Introduction To Genetic Algorithms (GAs) GA Operators and Parameters Genetic Algorithms To Solve The Traveling Salesman.](https://reader034.fdocuments.in/reader034/viewer/2022051316/5697bf7a1a28abf838c82b25/html5/thumbnails/20.jpg)
AlgorithmGenerate Initial Populationdo Calculate the Fitness of each member do { Select Parents from current population Perform Crossover add offspring to the new population Merge new population into the current population Mutate current population till result
is obtained }
![Page 21: Genetic Algorithms. 2 Overview Introduction To Genetic Algorithms (GAs) GA Operators and Parameters Genetic Algorithms To Solve The Traveling Salesman.](https://reader034.fdocuments.in/reader034/viewer/2022051316/5697bf7a1a28abf838c82b25/html5/thumbnails/21.jpg)
AlgorithmGenerate Initial Populationdo Calculate the Fitness of each member do { Select Parents from current population Perform Crossover add offspring to the new population Merge new population into the current population Mutate current population till result
is obtained }
![Page 22: Genetic Algorithms. 2 Overview Introduction To Genetic Algorithms (GAs) GA Operators and Parameters Genetic Algorithms To Solve The Traveling Salesman.](https://reader034.fdocuments.in/reader034/viewer/2022051316/5697bf7a1a28abf838c82b25/html5/thumbnails/22.jpg)
Fitness Function
A fitness function quantifies the optimality of a solution so that that particular solution may be ranked against all the other solutions.
A fitness value is assigned to each solution depending on how close it actually is to solving the problem.
Ideal fitness function correlates closely to goal + quickly computable.
![Page 23: Genetic Algorithms. 2 Overview Introduction To Genetic Algorithms (GAs) GA Operators and Parameters Genetic Algorithms To Solve The Traveling Salesman.](https://reader034.fdocuments.in/reader034/viewer/2022051316/5697bf7a1a28abf838c82b25/html5/thumbnails/23.jpg)
AlgorithmGenerate Initial Populationdo Calculate the Fitness of each member do { Select Parents from current population Perform Crossover add offspring to the new population Merge new population into the current population Mutate current population till result
is obtained }
![Page 24: Genetic Algorithms. 2 Overview Introduction To Genetic Algorithms (GAs) GA Operators and Parameters Genetic Algorithms To Solve The Traveling Salesman.](https://reader034.fdocuments.in/reader034/viewer/2022051316/5697bf7a1a28abf838c82b25/html5/thumbnails/24.jpg)
AlgorithmGenerate Initial Populationdo Calculate the Fitness of each member do { Select Parents from current population Perform Crossover add offspring to the new population Merge new population into the current population Mutate current population till result
is obtained }
![Page 25: Genetic Algorithms. 2 Overview Introduction To Genetic Algorithms (GAs) GA Operators and Parameters Genetic Algorithms To Solve The Traveling Salesman.](https://reader034.fdocuments.in/reader034/viewer/2022051316/5697bf7a1a28abf838c82b25/html5/thumbnails/25.jpg)
Crossover
Mimics biological recombination
Some portion of genetic material is swapped between chromosomes
Typically the swapping produces an offspring
![Page 26: Genetic Algorithms. 2 Overview Introduction To Genetic Algorithms (GAs) GA Operators and Parameters Genetic Algorithms To Solve The Traveling Salesman.](https://reader034.fdocuments.in/reader034/viewer/2022051316/5697bf7a1a28abf838c82b25/html5/thumbnails/26.jpg)
CROSSOVER
(1 2 9 3 0 7 ) (1 2 9 7 9 5)
(4 6 1 7 9 5 )
(1 2 9 3 0 7 )( 4 6 1 7 9 5)
(4 6 1 7 9 5 )
![Page 27: Genetic Algorithms. 2 Overview Introduction To Genetic Algorithms (GAs) GA Operators and Parameters Genetic Algorithms To Solve The Traveling Salesman.](https://reader034.fdocuments.in/reader034/viewer/2022051316/5697bf7a1a28abf838c82b25/html5/thumbnails/27.jpg)
AlgorithmGenerate Initial Populationdo Calculate the Fitness of each member do { Select Parents from current population Perform Crossover add offspring to the new population Merge new population into the current population Mutate current population till result
is obtained }
![Page 28: Genetic Algorithms. 2 Overview Introduction To Genetic Algorithms (GAs) GA Operators and Parameters Genetic Algorithms To Solve The Traveling Salesman.](https://reader034.fdocuments.in/reader034/viewer/2022051316/5697bf7a1a28abf838c82b25/html5/thumbnails/28.jpg)
AlgorithmGenerate Initial Populationdo Calculate the Fitness of each member do { Select Parents from current population Perform Crossover add offspring to the new population Merge new population into the current population Mutate current population till result
is obtained }
![Page 29: Genetic Algorithms. 2 Overview Introduction To Genetic Algorithms (GAs) GA Operators and Parameters Genetic Algorithms To Solve The Traveling Salesman.](https://reader034.fdocuments.in/reader034/viewer/2022051316/5697bf7a1a28abf838c82b25/html5/thumbnails/29.jpg)
Mutation
Selects a random locus – gene location – with some probability and alters the allele at that locus
The intuitive mechanism for the preservation of variety in the population
![Page 30: Genetic Algorithms. 2 Overview Introduction To Genetic Algorithms (GAs) GA Operators and Parameters Genetic Algorithms To Solve The Traveling Salesman.](https://reader034.fdocuments.in/reader034/viewer/2022051316/5697bf7a1a28abf838c82b25/html5/thumbnails/30.jpg)
Mutation: Local Modification
Before: (1 0 1 1 0 1 1 0)After: (0 1 1 0 0 1 1 0)
Before: (1.38 -69.4 326.44 0.1)After: (1.38 -67.5 326.44 0.1)
![Page 31: Genetic Algorithms. 2 Overview Introduction To Genetic Algorithms (GAs) GA Operators and Parameters Genetic Algorithms To Solve The Traveling Salesman.](https://reader034.fdocuments.in/reader034/viewer/2022051316/5697bf7a1a28abf838c82b25/html5/thumbnails/31.jpg)
The ProblemThe Traveling Salesman Problem is defined as: Given: 1) A set of cities 2) Symmetric distance matrix that indicates the cost of
travel from each city to every other city.
Goal: 1) Find the shortest circular tour, visiting every city
exactly once. 2) Minimize the total travel cost, which includes the
cost of traveling from the last city back to the first city’.
![Page 32: Genetic Algorithms. 2 Overview Introduction To Genetic Algorithms (GAs) GA Operators and Parameters Genetic Algorithms To Solve The Traveling Salesman.](https://reader034.fdocuments.in/reader034/viewer/2022051316/5697bf7a1a28abf838c82b25/html5/thumbnails/32.jpg)
Traveling Salesperson Problem
![Page 33: Genetic Algorithms. 2 Overview Introduction To Genetic Algorithms (GAs) GA Operators and Parameters Genetic Algorithms To Solve The Traveling Salesman.](https://reader034.fdocuments.in/reader034/viewer/2022051316/5697bf7a1a28abf838c82b25/html5/thumbnails/33.jpg)
34
Encoding
Represent every city with an integer .
Consider 6 Indian cities – Mumbai, Nagpur , Calcutta, Delhi, Bangalore
and Pune assign a number to each.
Mumbai 1Nagpur 2Calcutta 3Delhi 4Bangalore 5Pune 6
![Page 34: Genetic Algorithms. 2 Overview Introduction To Genetic Algorithms (GAs) GA Operators and Parameters Genetic Algorithms To Solve The Traveling Salesman.](https://reader034.fdocuments.in/reader034/viewer/2022051316/5697bf7a1a28abf838c82b25/html5/thumbnails/34.jpg)
35
Encoding
Thus a path would be represented as a sequence of integers from 1 to 6.
The path [1 2 3 4 5 6] represents a path from
Mumbai to Nagpur - Nagpur to Calcutta - Calcutta to Delhi - Delhi to Bangalore - Bangalore to Pune and pune to Mumbai.
![Page 35: Genetic Algorithms. 2 Overview Introduction To Genetic Algorithms (GAs) GA Operators and Parameters Genetic Algorithms To Solve The Traveling Salesman.](https://reader034.fdocuments.in/reader034/viewer/2022051316/5697bf7a1a28abf838c82b25/html5/thumbnails/35.jpg)
Fitness Function
The fitness function will be the total cost of the tour represented by each chromosome.
This can be calculated as the sum of the distances traversed in each travel segment.
The Lesser The Sum, The Fitter The Solution Represented By That Chromosome.
![Page 36: Genetic Algorithms. 2 Overview Introduction To Genetic Algorithms (GAs) GA Operators and Parameters Genetic Algorithms To Solve The Traveling Salesman.](https://reader034.fdocuments.in/reader034/viewer/2022051316/5697bf7a1a28abf838c82b25/html5/thumbnails/36.jpg)
1 2 3 4 5 6
1 0 863 1987 1407 998 163
2 863 0 1124 1012 1049 620
3 1987 1124 0 1461 1881 1844
4 1407 1012 1461 0 2061 1437
5 998 1049 1881 2061 0 841
6 163 620 1844 1437 841 0
Distance/Cost Matrix For TSP
![Page 37: Genetic Algorithms. 2 Overview Introduction To Genetic Algorithms (GAs) GA Operators and Parameters Genetic Algorithms To Solve The Traveling Salesman.](https://reader034.fdocuments.in/reader034/viewer/2022051316/5697bf7a1a28abf838c82b25/html5/thumbnails/37.jpg)
Fitness Function (contd.)
So, for a chromosome [4 1 3 2 5 6 ], the total cost of travel or fitness will be calculated as shown below
Fitness = 1407 + 1987 + 1124 + 1049 + 841 = 6408 kms.
Since our objective is to Minimize the distance, the lesser the total distance, the fitter the solution.
![Page 38: Genetic Algorithms. 2 Overview Introduction To Genetic Algorithms (GAs) GA Operators and Parameters Genetic Algorithms To Solve The Traveling Salesman.](https://reader034.fdocuments.in/reader034/viewer/2022051316/5697bf7a1a28abf838c82b25/html5/thumbnails/38.jpg)
Initial Population for TSP
(5,3,4,6,2) (2,4,6,3,5) (4,3,6,5,2)
(2,3,4,6,5) (4,3,6,2,5) (3,4,5,2,6)
(3,5,4,6,2) (4,5,3,6,2) (5,4,2,3,6)
(4,6,3,2,5) (3,4,2,6,5) (3,6,5,1,4)
![Page 39: Genetic Algorithms. 2 Overview Introduction To Genetic Algorithms (GAs) GA Operators and Parameters Genetic Algorithms To Solve The Traveling Salesman.](https://reader034.fdocuments.in/reader034/viewer/2022051316/5697bf7a1a28abf838c82b25/html5/thumbnails/39.jpg)
Select Parents
(5,3,4,6,2) (2,4,6,3,5) (4,3,6,5,2)
(2,3,4,6,5) (4,3,6,2,5) (3,4,5,2,6)
(3,5,4,6,2) (4,5,3,6,2) (5,4,2,3,6)
(4,6,3,2,5) (3,4,2,6,5) (3,6,5,1,4)
Try to pick the better ones.
![Page 40: Genetic Algorithms. 2 Overview Introduction To Genetic Algorithms (GAs) GA Operators and Parameters Genetic Algorithms To Solve The Traveling Salesman.](https://reader034.fdocuments.in/reader034/viewer/2022051316/5697bf7a1a28abf838c82b25/html5/thumbnails/40.jpg)
Create Off-Spring – 1 point
(5,3,4,6,2) (2,4,6,3,5) (4,3,6,5,2)
(2,3,4,6,5) (4,3,6,2,5) (3,4,5,2,6)
(3,5,4,6,2) (4,5,3,6,2) (5,4,2,3,6)
(4,6,3,2,5) (3,4,2,6,5) (3,6,5,1,4)
(3,4,5,6,2)
![Page 41: Genetic Algorithms. 2 Overview Introduction To Genetic Algorithms (GAs) GA Operators and Parameters Genetic Algorithms To Solve The Traveling Salesman.](https://reader034.fdocuments.in/reader034/viewer/2022051316/5697bf7a1a28abf838c82b25/html5/thumbnails/41.jpg)
(3,4,5,6,2)
Create More Offspring
(5,3,4,6,2) (2,4,6,3,5) (4,3,6,5,2)
(2,3,4,6,5) (4,3,6,2,5) (3,4,5,2,6)
(3,5,4,6,2) (4,5,3,6,2) (5,4,2,3,6)
(4,6,3,2,5) (3,4,2,6,5) (3,6,5,1,4)
(5,4,2,6,3)
![Page 42: Genetic Algorithms. 2 Overview Introduction To Genetic Algorithms (GAs) GA Operators and Parameters Genetic Algorithms To Solve The Traveling Salesman.](https://reader034.fdocuments.in/reader034/viewer/2022051316/5697bf7a1a28abf838c82b25/html5/thumbnails/42.jpg)
(3,4,5,6,2) (5,4,2,6,3)
Mutate
(5,3,4,6,2) (2,4,6,3,5) (4,3,6,5,2)
(2,3,4,6,5) (4,3,6,2,5) (3,4,5,2,6)
(3,5,4,6,2) (4,5,3,6,2) (5,4,2,3,6)
(4,6,3,2,5) (3,4,2,6,5) (3,6,5,1,4)
![Page 43: Genetic Algorithms. 2 Overview Introduction To Genetic Algorithms (GAs) GA Operators and Parameters Genetic Algorithms To Solve The Traveling Salesman.](https://reader034.fdocuments.in/reader034/viewer/2022051316/5697bf7a1a28abf838c82b25/html5/thumbnails/43.jpg)
Mutate
(5,3,4,6,2) (2,4,6,3,5) (4,3,6,5,2)
(2,3,4,6,5) (2,3,6,4,5) (3,4,5,2,6)
(3,5,4,6,2) (4,5,3,6,2) (5,4,2,3,6)
(4,6,3,2,5) (3,4,2,6,5) (3,6,5,1,4)
(3,4,5,6,2) (5,4,2,6,3)
![Page 44: Genetic Algorithms. 2 Overview Introduction To Genetic Algorithms (GAs) GA Operators and Parameters Genetic Algorithms To Solve The Traveling Salesman.](https://reader034.fdocuments.in/reader034/viewer/2022051316/5697bf7a1a28abf838c82b25/html5/thumbnails/44.jpg)
Eliminate
(5,3,4,6,2) (2,4,6,3,5) (4,3,6,5,2)
(2,3,4,6,5) (2,3,6,4,5) (3,4,5,2,6)
(3,5,4,6,2) (4,5,3,6,2) (5,4,2,3,6)
(4,6,3,2,5) (3,4,2,6,5) (3,6,5,1,4)
Tend to kill off the worst ones.
(3,4,5,6,2) (5,4,2,6,3)
![Page 45: Genetic Algorithms. 2 Overview Introduction To Genetic Algorithms (GAs) GA Operators and Parameters Genetic Algorithms To Solve The Traveling Salesman.](https://reader034.fdocuments.in/reader034/viewer/2022051316/5697bf7a1a28abf838c82b25/html5/thumbnails/45.jpg)
Integrate
(5,3,4,6,2) (2,4,6,3,5)
(2,3,6,4,5) (3,4,5,2,6)
(3,5,4,6,2) (4,5,3,6,2) (5,4,2,3,6)
(4,6,3,2,5) (3,4,2,6,5) (3,6,5,1,4)
(3,4,5,6,2)
(5,4,2,6,3)
![Page 46: Genetic Algorithms. 2 Overview Introduction To Genetic Algorithms (GAs) GA Operators and Parameters Genetic Algorithms To Solve The Traveling Salesman.](https://reader034.fdocuments.in/reader034/viewer/2022051316/5697bf7a1a28abf838c82b25/html5/thumbnails/46.jpg)
Restart
(5,3,4,6,2) (2,4,6,3,5)
(2,3,6,4,5) (3,4,5,2,6)
(3,5,4,6,2) (4,5,3,6,2) (5,4,2,3,6)
(4,6,3,2,5) (3,4,2,6,5) (3,6,5,1,4)
(3,4,5,6,2)
(5,4,2,6,3)
![Page 47: Genetic Algorithms. 2 Overview Introduction To Genetic Algorithms (GAs) GA Operators and Parameters Genetic Algorithms To Solve The Traveling Salesman.](https://reader034.fdocuments.in/reader034/viewer/2022051316/5697bf7a1a28abf838c82b25/html5/thumbnails/47.jpg)
When to Use a GA Alternate solutions are too slow or overly
complicated Need an exploratory tool to examine new
approaches Problem is similar to one that has already
been successfully solved by using a GA Want to hybridize with an existing solution Benefits of the GA technology meet key
problem requirements
![Page 48: Genetic Algorithms. 2 Overview Introduction To Genetic Algorithms (GAs) GA Operators and Parameters Genetic Algorithms To Solve The Traveling Salesman.](https://reader034.fdocuments.in/reader034/viewer/2022051316/5697bf7a1a28abf838c82b25/html5/thumbnails/48.jpg)
49
SUMMARY
Genetic Algorithms (GAs) implement optimization strategies based on simulation of the natural law of evolution of a species by natural selection
The basic GA Operators are:EncodingRecombinationCrossoverMutation
GAs have been applied to a variety of function optimization problems, and have been shown to be highly effective in searching a large, poorly defined search space
![Page 49: Genetic Algorithms. 2 Overview Introduction To Genetic Algorithms (GAs) GA Operators and Parameters Genetic Algorithms To Solve The Traveling Salesman.](https://reader034.fdocuments.in/reader034/viewer/2022051316/5697bf7a1a28abf838c82b25/html5/thumbnails/49.jpg)
THANK YOU