1. Genetic Algorithms
-By kapil khatiwada
• BIM 5th Semester Class Presentation
Artificial Intelligence [IT 228]
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2. A brief. .
• In AI the term used for an agent is intelligent agent.
• Learning is an Important feature of Intelligence.
• We need machine learning to understand & improve
efficiency of human being.
• One of paradigms of Machine Learning is Genetic
Algorithm.
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3. What is Genetic Algorithm(GA)?
 GA’s are Adaptive heuristic search algorithm based
on the evolutionary idea of natural selection &
genetics.
 A search technique used in computing to find true
or approximate solutions to optimization and
search problems.
 GA generates a set of possible solutions and
evaluates each in order to decide which solutions
are fit for evolving answer.
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4.  Before using GA , potential solutions are encoded
to problems
 This could be as a binary bit string
 It is referred to as the chromosome
101010010011101010101
• Everything in computers is represented in
binary
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5. Simple Genetic Algorithm
Step 1: randomly initialize population
Step 2: determine fitness of population
Step 3: repeat until the termination criteria is not
satisfied
Step 4: select parents for reproduction
Step 5: perform recombination and mutation
Step 6: evaluate fitness of population
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6. Genetic Algorithms
Operation/Operators
1. EVALUATION
2. SELECTION
3. CROSSOVER
4.MUTATION
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7. Operation Of GA
• At the beginning of a run of a genetic algorithm a
large population
created.
of random chromosomes is
10010101110101001010011101101
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8. Evaluation
• When every new population is created each member
is evaluated for it’s fitness by testing for some
attribute.
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9. Selection/Reproduction
 Individuals are selected at random in groups after
they are evaluated for their fitness and the
individuals with the highest fitness within these
groups are used to populate the new generation.
 Better the fitness, bigger the chance to be
selected.
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10. Crossover/Recombination
• Crossover is used to produce the new members of a
population by recombining parents
• Typically, crossover takes two parents, cuts their
chromosome strings at a randomly chosen position, swaps
the head (or tail) segments to produce two offsprings.
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11. 
There is a chance that the chromosomes of the
two parents are copied unmodified as offspring
• If crossover is not applied, offspring are
produced by duplicating their parents (no
disruption).
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12. Mutation
• Mutation occurs to some of the genes in the new
population.
• A single parent produces a offspring. i.e asexual
reproduction can also result in successful evolution.
• A point is picked at random within a chromosome
and the mutation that occurs is random.
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13. Conclusion
Question:
‘If GAs are so smart, why aren’t they rich?’
Answer: ‘Genetic algorithms are rich -rich in application
across a large and growing number of disciplines.’
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14. Thanks for your attention!
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