Artificial Intelligence · Unit 9
Evolutionary and Reinforcement Learning
Exam-focused notes for Evolutionary and Reinforcement Learning (Artificial Intelligence, BIT252): what the TU syllabus asks and how it has actually been tested, with 5 solved past questions from this unit.
What this unit covers
- Genetic algorithm operators
- Genetic algorithm learning process
- Reinforcement learning
- Rewards and punishment in learning
- Learning by analogy
Genetic algorithm learning process
How learning by genetic algorithm is performed? [5]
A Genetic Algorithm (GA) is a search and optimization technique inspired by the process of natural selection and biological evolution. In machine learning, GAs are used to evolve solutions (hypotheses) over generations to find the best-fit model for a given...
Full solved answer →Genetic algorithm operators
How does Genetic algorithm work? Explain. [5]
A Genetic Algorithm (GA) is a search and optimization technique inspired by the process of natural selection and biological evolution. It works by evolving a population of candidate solutions over successive generations to find an optimal or near-optimal so...
Full solved answer →Describe the different operators used in genetic algorithm. [5]
--- Genetic Algorithms use three primary operators to evolve a population of candidate solutions toward an optimal solution. These operators mimic the process of natural evolution. --- Purpose: Selects individuals (chromosomes) from the current population t...
Full solved answer →Learning by analogy
Describe the concept of learning by analogy with an example. [5]
Learning by analogy is a type of machine learning in which a system acquires new knowledge or skills by recognizing structural or functional similarities between a new, unfamiliar problem (target domain) and a previously known problem (source domain). The s...
Full solved answer →Rewards and punishment in learning
What is the role of rewards and punishment in reinforcement learning? Describe with an example. [5]
Reinforcement Learning (RL) is a type of machine learning where an agent learns to make decisions by interacting with an environment. The agent learns through trial and error, guided by feedback in the form of rewards and punishments. --- A reward is a posi...
Full solved answer →Make Unit 9 stick
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