Podcast
Questions and Answers
What is the final solution path obtained from the given iterations?
What is the final solution path obtained from the given iterations?
What is the time complexity of Greedy best-first search in the worst case?
What is the time complexity of Greedy best-first search in the worst case?
What is the space complexity of Greedy best-first search in the worst case?
What is the space complexity of Greedy best-first search in the worst case?
Is Greedy best-first search complete?
Is Greedy best-first search complete?
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Is Greedy best-first search optimal?
Is Greedy best-first search optimal?
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What is A* search algorithm?
What is A* search algorithm?
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What is the evaluation function used in A* search algorithm?
What is the evaluation function used in A* search algorithm?
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What happens when the goal node is found in A* search algorithm?
What happens when the goal node is found in A* search algorithm?
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What is the purpose of the OPEN list in A* search algorithm?
What is the purpose of the OPEN list in A* search algorithm?
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What happens to a node when it is expanded in A* search algorithm?
What happens to a node when it is expanded in A* search algorithm?
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Study Notes
The use of GA in the field of robotics
- Genetic Algorithm can be used to find the best path for autonomous mobile robots.
- Genetic Algorithm can be used to find the optimal value for time of recharging.
- Genetic Algorithm can be used for Automatic Recharging Path Planning for Cleaning Robots.
General implementation of the Genetic Algorithm
- This is a general implementation of the Genetic Algorithm.
Types of search algorithms
- Search algorithms can be classified into two categories: uninformed (Blind search) and informed search (Heuristic search).
- Uninformed search does not contain any domain knowledge and operates in a brute-force way.
- Informed search uses domain knowledge to guide the search.
Uninformed/Blind Search
- Uninformed search only includes information about how to traverse the tree and how to identify leaf and goal nodes.
- It is also called blind search.
A* Search Algorithm
- A* search algorithm is optimal if it is admissible and consistent.
- Admissible heuristic is optimistic in nature.
- Consistency is required for A* graph-search.
- Time complexity of A* search algorithm depends on the heuristic function and the number of nodes expanded.
- Space complexity of A* search algorithm is O(b^d)f(n).
Using a Genetic Algorithm to Explore A*-like Path Finding Algorithms
- The parameters of A* search are considered as chromosome parameters for Genetic Algorithm population.
- The final solution path is found using iteration and expansion of nodes.
Best-first Search Algorithm (Greedy Search)
- Time complexity of Greedy best-first search is O(bm).
- Space complexity of Greedy best-first search is O(bm).
- Greedy best-first search is incomplete and not optimal.
A* Search Algorithm
- A* search uses heuristic function h(n) and cost to reach the node n from the start state g(n).
- The algorithm terminates when the goal node is found.
- A* search algorithm is a combination of search heuristic and the cost to reach the node.
- The algorithm uses a fitness number, which is the sum of the cost and heuristic function.
- At each point in the search space, only the node with the lowest value of f(n) is expanded.
- The algorithm follows a step-by-step process to find the goal node.
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Description
This quiz covers the use of Genetic Algorithm in the field of robotics, including its applications and examples. It is part of a comprehensive course on Genetic Algorithm, covering topics from introduction to machine learning and medicine.
