23.10.2019 13:30 - 23.10.2019 15:30
Ayazağa Kampüsü, Ayazağa Kampüsü
Akademik
Introduction to Optimization
A. Phases of an optimization study
1. Define the problem and gather relevant data.
2. Formulate a mathematical model for the problem.
3. Develop a computer algorithm for deriving solutions to the problem from the model.
4. Test the model and refine it as needed.
5. Prepare the ongoing application of the model as prescribed by management.
6. Implement.
Usually some cycles are necessary.
B. Examples
Multi-Objective Optimization with Metaheuristics: Genetic Algorithms (GA)
1. Multi-objective problem
2. What is an “optimum”? Pareto-optimality. Global Pareto front.
3. Multi-objective GA approaches
4. Examples
Neural Networks
A. What is a Neural Network? A neural network is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates.
B. How to program a Neural Network? Introduction to various Neural Networks in order to gain technical insight on how to build different kinds of Neural Networks, evaluate their performance and use them to solve complex problems.
C. Simple artificial neural networks (ANN) to build connections between cellular and network level phenomena and higher-order processes like thought and behavior. Simple two-layer neural network models are able to associate an input pattern with an output pattern.
D. Examples: Optimizing ANN parameters using Genetic Algorithms.