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courses:a4m33bia [2016/06/16 23:05] matejch [2016, 2. 6.] |
courses:a4m33bia [2025/01/03 18:23] (aktuální) |
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| Řádek 204: | Řádek 204: | ||
| <code> | <code> | ||
| - | 1. SOM | + | 1. Self-Organizing Maps (SOM) [3 pts] |
| + | • Explain SOM network architecture and learning. | ||
| + | • Draw the SOM for 2D input and 5x5 grid of neurons. | ||
| + | |||
| + | 2. Recurrent Neural Netowrk (RNN) [3 pts] | ||
| + | • Describe fully-connected RNN architecture. | ||
| + | • Describe evaluation and learning of RNN. | ||
| + | • What is synchronous/asynchronous network evaluation? | ||
| + | |||
| + | 3. Neuro-evolution [3 pts] | ||
| + | • Discuss mutation and crossover in neuro-evolution. | ||
| + | |||
| + | 4. Hypercube-based NeuroEvolution of Augmenting Topologies (HyperNEAT) [4 pts] | ||
| + | • Describe HyperNEAT algorithm. | ||
| + | • What is the difference between HyperNEAT and standard evolution of ANNs? | ||
| + | |||
| + | 5. Overfitting [4 pts] | ||
| + | • How can you prevent overfitting of artificial neural network? | ||
| + | |||
| + | 6. Perceptron vs Radial Basis Function (RBF) type neurons [3 pts] | ||
| + | • Describe differences between perceptron and RBF neurons. | ||
| + | • Draw an illustrative picture of how they divide an input space. | ||
| + | |||
| + | 7. EAs for Multi-Objective Optimizations [4 pts] | ||
| + | • Write a definition of domination. | ||
| + | • Describe two goals of the multi-objective optimization. What are the two desired properties of the final set of solutions? | ||
| + | |||
| + | • Lets assume a set of all feasible solution in the left figure below. Draw a complete Pareto-optimal set in the figure given the minimization objective o1 and minimization objective o2. | ||
| + | • Lets assume the set of candidate solutions in the right figure. Draw the first four fronts of non-dominated solutions given the minimization objective o1 and minimization objective o2. | ||
| + | { two pictures, first figure - space, second figure - points } | ||
| + | |||
| + | 8. Ant Colony Optimization for Continuous Domain (ACOr) [3 pts] | ||
| + | • Describe the ACOr algorithm | ||
| + | • Describe the way the Gaussian kernel probabilty density function (PDF) is used to model the pheromone. | ||
| + | • Describe how the Gaussian kernel PDF is modelled and how its parameters are estimated. | ||
| + | |||
| + | 9. Roulette Wheel [4 pts] | ||
| + | • Describe the roulette wheel selection method. | ||
| + | • Given a population of 5 individuals with the following fitness value - fitness(A)=1, fitness(B)=2, fitness(C)=3, fitness(D)=5, fitness(E)=9 - determine an expected number of copies that solution A and E recieve among ten solutions samples using a roulette wheel selection method. What is the range of the actual number of copies of A and E out of the ten samples solutions? | ||
| + | |||
| + | 10. Dynamic Optimizations [3 pts] | ||
| + | • Desribe the principles of the GA with Real-coded Binary Representation (GARB). | ||
| + | • Redundant representaion | ||
| + | • Gene-strength adaptation | ||
| + | |||
| + | 11. Ant Colony Optmimization (ACO) [3 pts] | ||
| + | • Describe the following formula used for probabilistic decision making in ACO algorithm. How is it used in ACO algorithm? | ||
| + | • Explain a meaning of the symbols Tau, Eta, alpha, beta, tabuk. | ||
| + | |||
| + | { p_ij^k = ... formula } | ||
| + | |||
| + | 12. Performance Metrics for Multi-Objective Optimizations [3 pts] | ||
| + | • Describe the S metric (i.e. the size of the space covered) used to assess a quality of a set of non-dominated solutions. | ||
| + | • What are its advantages and disadvantages? | ||
| + | • Illustrate it on an example. | ||
| </code> | </code> | ||
| ~~DISCUSSION~~ | ~~DISCUSSION~~ | ||
