This presentation contains an outline of the Genetic Algorithm and its applications.

1. Introduction: Genetic Algorithm
Adri Jovin J J, M.Tech., Ph.D.
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2. Soft Computing
• Introduced by Lotfi A. Zadeh, University of California, Berkley
• Collection of computational methods
• Includes Fuzzy Systems, Neural Networks and Evolutionary Algorithms
• Deployment of soft computing for the solution of machine learning problems has led to high Machine Intelligence
Quotient
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Image Credit: Electrical Engineering and Computer Sciences, UC, Berkeley
“Soft computing differs from hard computing (conventional computing) in its tolerance to
imprecision, uncertainty and partial truth”
-Lotfi A. Zadeh

3. Soft Computing (Contd…)
Fuzzy Systems
Neural
Networks
Evolutionary
Algorithms
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Fuzzy-evolutionary hybrids Neuro-fuzzy hybrids
Neuro-evolutionary hybrids
Neuro-fuzzy-evolutionary hybrids

4. Flowchart for genetic algorithm
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Start
Initial
Population
Selection
Crossover
Mutation
Terminate?
Best Individuals
Output
Stop
Evolution
Yes
No

5. Single-site Crossover
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6. Two-point crossover
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7. Multi-point crossover
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8. References
Rajasekaran, S., & Pai, G. V. (2017). Neural Networks, Fuzzy Systems and Evolutionary Algorithms: Synthesis and
Applications. PHI Learning Pvt. Ltd..
Haykin, S. (2010). Neural Networks and Learning Machines, 3/E. Pearson Education India.
Sivanandam, S. N., & Deepa, S. N. (2007). Principles of soft computing. John Wiley & Sons.
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