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About The Course
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Description
This course enables learning on different graph traversal techniques (BFS & DFS)
along with enhanced search algorithms like A* algorithm. Genetic algorithms are discussed along with Min-Max algorithms.
Expert systems and ANN are also discussed in detail along with Fuzzy logic in SC.
Along with a pdf with important notes and explanations
Modules Covered:
Introduction to AI / SC
Problem solving algorithms
Knowledge, Reasoning and Planning.
Fuzzy Logic.
Artificial Neural Network.
Expert System. -
How to Pass AISC
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Introduction to Artificial Intelligence(AI) and Soft Computing
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Problem Solving
- BFS ( Breadth First Search ) Algorithm with solved Example
- DFS ( Depth First Search ) Algorithm with solved Example
- IDFS ( Iterative Depth First Search ) Algorithm with solved Example
- GBFS Solved Example
- A Star solved Example
- Hill Climbing
- Min Max Solved Example
- Alpha-Beta Pruning Solved Example
- Genetic Algorithm
- Genetic Algorithm Max one Problem Solved Example
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Fuzzy Logic
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Knowledge, Reasoning and Planning
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Artificial Neural Network
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Expert System
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Notes
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Extra Notes
- Introduction to Artificial Intelligence and Soft Computing (Module 1 Notes)
- Artificial Intelligence Notes #1
- Artificial Intelligence Notes #2
- Soft Computing Module 4
- Soft Computing Module 5
- Soft Computing Handmade Notes
- Artificial Intelligence and Soft Computing Complete Notes ( Toppers Solution )
- Mobile Communication and Computing Notes ( Toppers Solution )
- Digital Signal Processing Handmade Notes
Genetic Algorithm Max one Problem Solved Example
Genetic Algorithm Max one Problem Solved Example
Max One Problem is the simplest problem that only performs the calculation of the maximum value from a number of binary strings. Usually, Max One Problem is used to represent an algorithm i.e. Genetic Algorithm. Later, the result will be compared with conventional genetic algorithms. In this video we have taken an example of a Genetic Algorithm and explain the different steps involved in Genetic algorithm
Five phases are considered in a genetic algorithm.
- Initial population
- Fitness function
- Selection
- Crossover
- Mutation
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