BSc: Nature Inspired Computing
Nature Inspired Computing
- Course name: Nature Inspired Computing
- Code discipline: CSE340
- Subject area:
Short Description
This course covers the following concepts: .
Prerequisites
Prerequisite subjects
Prerequisite topics
Course Topics
| Section | Topics within the section |
|---|---|
| Basic concepts of Nature Inspired Computing |
|
| Computational complexity, ANN |
|
| Optimization, PSO |
|
Intended Learning Outcomes (ILOs)
What is the main purpose of this course?
The main purpose of this is to introduce methods and algorithms inspired by naturally occurring phenomena and applying them to optimization, design and learning problems. The course focuses on learning implementation and analysis of such algorithms. These include evolutionary computation, swarm intelligence and neural networks.
ILOs defined at three levels
Level 1: What concepts should a student know/remember/explain?
By the end of the course, the students should be able to ...
- Basic concepts in Swarm intelligence concepts
- Basic concepts in Global optimization
- Basic concepts in Classification methods
Level 2: What basic practical skills should a student be able to perform?
By the end of the course, the students should be able to ...
- Computational complexity
- Methods of computing computational complexity
- Methods of optimization in swarm intelligence
Level 3: What complex comprehensive skills should a student be able to apply in real-life scenarios?
By the end of the course, the students should be able to ...
- Reductions
- Empirical Algorithms
- Evolutionary Algorithms
- Co-evolutionary Algorithms
- Genetic Programming
Grading
Course grading range
| Grade | Range | Description of performance |
|---|---|---|
| A. Excellent | 90-100 | - |
| B. Good | 75-89 | - |
| C. Satisfactory | 60-74 | - |
| D. Poor | 0-55 | - |
Course activities and grading breakdown
| Activity Type | Percentage of the overall course grade |
|---|---|
| Labs/seminar classes | 20 |
| Interim performance assessment (class participation) | 30 |
| Exams | 50 |
Recommendations for students on how to succeed in the course
Resources, literature and reference materials
Open access resources
- Textbook: Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies, D. Floreano and C. Mattiussi (2008), MIT Press.
- Textbook: Siddique, N. and Adeli, H., 2015. Nature inspired computing: an overview and some future directions. Cognitive computation, 7(6), pp.706-714.
- Textbook: Coello, C.C., Dhaenens, C. and Jourdan, L. eds., 2009. Advances in multi-objective nature inspired computing (Vol. 272). Springer.
- Textbook: Khosravy, M., Gupta, N., Patel, N. and Senjyu, T. eds., 2020. Frontier applications of nature inspired computation. Springer Nature.
Closed access resources
Software and tools used within the course
Teaching Methodology: Methods, techniques, & activities
Activities and Teaching Methods
| Learning Activities | Section 1 | Section 2 | Section 3 |
|---|---|---|---|
| Development of individual parts of software product code | 1 | 1 | 1 |
| Homework and group projects | 1 | 1 | 1 |
| Midterm evaluation | 1 | 1 | 1 |
| Testing (written or computer based) | 1 | 1 | 1 |
| Discussions | 1 | 1 | 1 |
Formative Assessment and Course Activities
Ongoing performance assessment
Section 1
| Activity Type | Content | Is Graded? |
|---|---|---|
| Question | What is an approximation? | 1 |
| Question | What is a heuristics? | 1 |
| Question | What are the main Characteristics of Natural Systems/Algorithms? | 1 |
| Question | What are Cellular Automata? | 1 |
| Question | Give examples of Evolutionary Algorithms | 1 |
| Question | Provide an example of approximation? | 0 |
| Question | What is the difference between Approximation algorithms and heuristics | 0 |
| Question | Write an Evolutionary Algorithm as a pseudocode | 0 |
| Question | Write a Genetic Algorithm as a pseudocode | 0 |
| Question | What are the characteristics of Genetic Algorithms | 0 |
Section 2
| Activity Type | Content | Is Graded? |
|---|---|---|
| Question | What are Turing machines? | 1 |
| Question | What is computational complexity? | 1 |
| Question | What are artificial neural networks? | 1 |
| Question | What are Recurrent ANN? | 1 |
| Question | What is deep learning? | 1 |
| Question | Give examples of algorithms of different computational complexities | 0 |
| Question | Give example of how one computes computational complexity | 0 |
| Question | What is a basic structure of an ANN? | 0 |
| Question | What is a fully connected ANN? | 0 |
| Question | Draw structure of LSTM RNN | 0 |
Section 3
| Activity Type | Content | Is Graded? |
|---|---|---|
| Question | What is a Foraging Algorithm | 1 |
| Question | What is PSO? | 1 |
| Question | What is a multi-objective optimization? | 1 |
| Question | What is Ant Colony Optimization? | 1 |
| Question | Write PSO as a diagram | 0 |
| Question | Give example of a problem that can be solved using PSO | 0 |
| Question | Write a code implementing PSO for a problem with two variables | 0 |
| Question | How does the number of particles influence PSO performance? | 0 |
| Question | How do local minima influence PSO performance? | 0 |
Final assessment
Section 1
- Genetic Algorithms can be used for:
a. Optimization (correct) b. Feedback Control c. Automatic Differentiation d. Numeric Geometry
- Applications of evolutionary algorithms do not include
a. Robotics b. Design c. Optimization d. Measurements (correct)
- Which of the following algorithms suffers from local minima of the objective function
a. Genetic algorithm b. Evolutionary algorithm c. Gradient descent (correct) d. All three of the above Section 2
- Which of the following are neural networks architectures?
a. ANN, RNN, CNN b. RNN, CNN, LSTM c. RNN, CNN, fully connected (correct) d. Reinforcement learning, Supervised learning
- Which types of machine learning can be used to learn policies for agents acting in dynamic environments, where marked datasets are not available?
a. Reinforcement learning, evolutionary algorithms (correct) b. YOLO c. Cauterization d. Supervised learning
- What distinguishes deep learning?
a. The number of layers of the network, and the methods of gradient propagation aiming at lowering the effects of the vanishing gradients (correct) b. Fully-connected layers c. LSTM cells d. Auto-encoding layers Section 3
- Which of the following can be used to solve optimization problems?
a. PSO, Genetic algorithm (correct) b. CNN, PSO c. CNN d. RNN, PSO
- Which of the following can be used to find acceptable parameters of a process, given inequality constraints?
a. PSO, Genetic algorithm (correct) b. CNN, PSO c. CNN d. RNN, PSO
The retake exam
Section 1
Section 2
Section 3