Instructions: Answer the following questions in two to three sentences, based on the provided source material.

1. What is the core concept behind a Genetic Algorithm (GA), and from what fields is it derived?
2. Explain the primary purpose of a fitness function within a Genetic Algorithm.
3. Describe the phenomenon of “premature convergence” and why it is an undesirable condition in a GA.
4. What is the fundamental difference between the Generational and Steady State population models?
5. Why are Genetic Algorithms considered particularly useful for solving NP-Hard problems?
6. Explain the purpose of the mutation operator and the risk associated with setting its probability too high.
7. What is “Elitism” in the context of survivor selection, and what does it guarantee?
8. Contrast the Lamarckian and Darwinian models of lifetime adaptation as they apply to computational GAs.
9. Describe how Tournament Selection works as a parent selection method.
10. What is the Building Block Hypothesis and what does it suggest about how GAs achieve success?