Genetic algorithms (GA) are a class of optimization algorithms inspired by biological evolution. GAs use concepts like natural selection and genetic inheritance to evolve solutions to problems by iteratively selecting better solutions. A GA encodes potential solutions as strings called chromosomes and uses genetic operators like crossover and mutation to generate new solutions, evaluating them to select the fittest ones. This process is repeated until a termination condition is reached, such as a solution meeting criteria or a fixed number of generations. GAs are well-suited for complex problems where little is known about the search space.
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Introduction to Genetic Algorithms
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Introduction to
Genetic Algorithms
BY
PREMSANKAR.C
CS S7
ROLL NO :25
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Genetic Algorithms (GA) Overview
Originally developed by John Holland (1975)
A class of optimization algorithms
Inspired by the biological evolution process
Uses concepts of “Natural Selection” and
“Genetic Inheritance” (Darwin 1859)
Particularly well suited for hard problems where
little is known about the underlying
search space
Widely-used in business, science and
engineering
GA’s are a subclass of Evolutionary Algorithm
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History of GA’s
Evolutionary computing developed in
the 1960’s.
GA’s were created by John Holland in
the mid-70’s.
The computer model introduces
simplifications (relative to the real
biological mechanisms)
General Introduction to GA’s
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INTRODUCTION
genetic algorithms are best for
searching for new solutions
making use of solutions that have
worked well in the past
It works on large population of
solutions that are repeatedly
subjected to selection pressure
(survival of the fittest)
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Each solution is encoded as a
chromosome (string) also called a
genotype
chromosome is given a measure of
fitness via a fitness function.
Possible information encoding
Bit strings (0101 ... 1100)
Real numbers (43.2 -33.1 ... 89.2)
Permutations of element (E11 E3 E7 ... E1 E15)
Lists of rules (R1 R2 R3 ... R22 R23)
Program elements (genetic programming)
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Classes of Search Techniques
Search Techniques
Calculus Base
Techniques
Guided random
search techniques
Enumerative
Techniques
BFSDFS Dynamic
Programmin
g
Tabu Search Hill
Climbing
Simulated
Anealing
Evolutionary
Algorithms
Genetic
Programming
Genetic
Algorithm
s
Fibonacci Sort
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Genetic Algorithms vs Traditional
Algorithm
1.GA’s work with a coding of parameter set,
not the parameter themselves.
2.GA’s search from a population of points,
not a single point.
3. Application of GA operators causes
information from the previous
generation to be carried over to the next.
4.GA’s use probabilistic rules, not
deterministic rules.
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BASIC Components of a GA
A problem definition as input, and
Encoding principles (gene, chromosome)
Initialization procedure (creation)
Selection of parents (reproduction)
Genetic operators (mutation, recombination)
Evaluation function (environment)
Termination condition
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Initialization
Start with a population of randomly generated
individuals, or use
- A previously saved population
- A set of solutions provided by a human expert
- A set of solutions provided by another heuristic
algorithm
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ENCODING
Each chromosome has one binary string.
Each bit in this string can represent some
characteristic of the solution.
The binary string of chromosome example
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FITTNESS FUNCTION
Determine the fitness of each
member of the population
Perform the objective function on
each population member
. FitnessScaling adjusts down the
fitness values of the super-
performers and adjusts up the lower
performers.
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Genetic Operators
Three major operations of genetic algorithm
are
Selection replicates the most successful
solutions found in a population
Recombination decomposes two distinct
solutions and then randomly mixes their parts
to form new solutions
Mutation randomly changes a candidate
solution(0-1)
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MUTATIONMUTATION
Purpose: to simulate the effect of errors that
happen with low probability during duplication
For binary encoding we can switch randomly
chosen bits from 1 to 0 or from 0 to 1.
Results:
- Movement in the search space
- Restoration of lost information to the population
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SELECTION(reproduction)
Purpose: to focus the search in
promising regions of the space
Inspiration: Darwin’s “survival of
the fittest” .
Example: the probability of
selecting a string with a fitness
value of f is f/ft, ft is the sum of all
of the fitness values in the
population
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CROSSOVERCROSSOVER (Recombination )
A. One-point crossover
B. Two-point crossover
•Crossover selects genes from parent chromosomes
and creates a new one
•choose some crossover point
•everything before this point copies from the first
parent and then everything after the crossover copies
from the second parent
•Causes an exchange of genetic material between
two parents
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Termination condition
A solution is found that satisfies minimum
criteria
Fixed number of generations reached
Allocated budget (computation
time/money) reached
The highest ranking solution's fitness is
reaching
A satisfactory solution has been achieved
No improvement in solution quality
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SIMPLE GENETIC ALGORITHM
1. Create a Random Initial State
2. Evaluate Fitness
3. Crossover ( recombination)
4. Reproduce
5. Repeat until successful.
6. Terminate
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THE EVOLUTIONARY CYCLE
selection
population evaluation
modification
discard
deleted
members
parents
modified
members
evaluated
initiate
evaluate
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Initial Population
Selection
Reproduction
Mutation
Next
Iteration (Generation)
Block diagram
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• Recombination (cross-over) can when using
bitstrings schematically be represented:
• Using a specific cross-over point
1
0
0
1
1
0
1
0
1
0
1
1
1
0
X
1
0
0
1
1
1
0
0
1
0
1
1
0
1
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• Mutation prevents the algorithm to be trapped in a
local minimum
• In the bitstring approach mutation is simpy the changing
of one of the bits
1
0
0
1
1
0
1
1
1
0
1
1
0
1
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ADVANTAGES OF GENETIC ALGORITHMS
A fastest search technique
GAs will produce "close" to optimal
results in a "reasonable" amount of
time
Suitable for parallel processing
Fairly simple to develop
Makes no assumptions about the
problem space
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DRAWBACKS
Number of permutations of functions and
variables. The search space is vast.
Most GPs are limited in the available
operators and terminals they can use.
It requires a lot of computer work, even
when a good set of operations, terminals
and controlling algorithm are chosen
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APPLICATIONS OF GENETIC ALGORITHMS
genetic programming
Scheduling: Facility, Production, Job, and
Transportation Scheduling
Design: Circuit board layout, Communication
Network design, keyboard layout, Parametric
design in aircraft
Machine Learning: Designing Neural
Networks, Classifier Systems, Learning rules
Image Processing: Pattern recognition
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CONCLUSION
GAs are a powerful tool for global
search
GA are best for searching for new
solutions and making use of
solutions that have worked well in
the past