32.7.4 - Genetic Algorithms
Enroll to start learning
You’ve not yet enrolled in this course. Please enroll for free to listen to audio lessons, classroom podcasts and take practice test.
Practice Questions
Test your understanding with targeted questions
What are genetic algorithms?
💡 Hint: Think of how nature works in terms of evolution.
Name one process involved in genetic algorithms.
💡 Hint: These processes are related to how new solutions are created.
4 more questions available
Interactive Quizzes
Quick quizzes to reinforce your learning
What do genetic algorithms mimic from nature?
💡 Hint: Think about how species evolve over time.
True or False: Mutation in GAs always produces the best solution.
💡 Hint: Consider what mutation means in both biology and algorithms.
1 more question available
Challenge Problems
Push your limits with advanced challenges
Propose a method for implementing genetic algorithms in optimizing the material mix of a new sustainable concrete designed for a high-impact construction project.
💡 Hint: Consider what factors you need to measure the performance of the mixes.
Critique the effectiveness of genetic algorithms versus traditional optimization methods in material selection for construction. What are the pros and cons?
💡 Hint: Think about the balance between efficiency gains and the risks associated with complexity.
Get performance evaluation
Reference links
Supplementary resources to enhance your learning experience.