Presentation topic: “Content-based Image Retrieval”

Presenter: Efstathios Chatzikyriakidis

PDF presentation: https://bit.ly/2Cp0fyM

Source code: https://bit.ly/2WaQFai

Presentation topic: “Content-based Image Retrieval”

Presenter: Efstathios Chatzikyriakidis

PDF presentation: https://bit.ly/2Cp0fyM

Source code: https://bit.ly/2WaQFai

Presentation topic: “Adversarial Face De-identification”

Presenter: Efstathios Chatzikyriakidis

PDF presentation: https://bit.ly/2W0o3QM

Experiments (exported files): https://bit.ly/2W7TlVQ

Presentation topic: “Adversarial Examples and Generative Adversarial Networks”

Presenter: Efstathios Chatzikyriakidis

Contributor: Christos Papaioannidis

PDF presentation: https://bit.ly/2D76MA3

This project aims to the development of an open-source experimental prototype for solving and generating Sudoku puzzles by using only the strength of Genetic Algorithms. This is not a general purpose GA framework but a specific GA implementation for solving and generating Sudoku puzzles. The mechanics of the GA are based on the theoretical scientific paper “Solving and Rating Sudoku Puzzles with Genetic Algorithms” of Timo Mantere and Janne Koljonen. From the first moment, I liked the paper. So, I implemented it in Python. Also, I have add some variations to the algorithm in order to be more efficient. This project can be used in order to solve or generate new NxN Sudoku puzzles with N sub-boxes (e.g. 4×4, 9×9, etc).

The project “PGASystem” (Parallel Genetic Algorithms System) is an under development system based on the client / server architecture and can be used to implement and study of parallel genetic algorithms.

**Thoughts on Automatic Software Repairing and Genetic Programming**

In the field of Software Engineering enough emphasis is given on the development of methodologies and mechanisms for the design of optimal software systems. Moreover, the quality of a software system can be assessed by carrying out appropriate metrics. Key features under study during the evaluation of a system are reliability, stability, security, portability and usability. The quality of a software system depends mainly on the time spent, expenses made, debugging and testing techniques used etc.

**Taxonomy**

The Ant Colony System algorithm is an example of an Ant Colony Optimization method from the field of Swarm Intelligence, Metaheuristics and Computational Intelligence. Ant Colony System is an extension to the Ant System algorithm and is related to other Ant Colony Optimization methods such as Elite Ant System, and Rank-based Ant System.

**Taxonomy**

The Cultural Algorithm is an extension to the field of Evolutionary Computation and may be considered a Meta-Evolutionary Algorithm. It more broadly belongs to the field of Computational Intelligence and Metaheuristics. It is related to other high-order extensions of Evolutionary Computation such as the Memetic Algorithm.

**Taxonomy**

Harmony Search belongs to the fields of Computational Intelligence and Metaheuristics.

**Taxonomy**

Memetic Algorithms have elements of Metaheuristics and Computational Intelligence. Although they have principles of Evolutionary Algorithms, they may not strictly be considered an Evolutionary Technique. Memetic Algorithms have functional similarities to Baldwinian Evolutionary Algorithms, Lamarckian Evolutionary Algorithms, Hybrid Evolutionary Algorithms, and Cultural Algorithms. Using ideas of memes and Memetic Algorithms in optimization may be referred to as Memetic Computing.

Within the framework of the course “**Computer Networks III – Theory**” (Department of Informatics and Communications, T.E.I. of Central Macedonia) we were asked to write a presentation related to the content of the course. The topic of my presentation was Genetic Routing.

I quote below a personal portable implementation (in C++) of a classic Differential Evolution algorithm used to maximize the function **f(x) = sin(x)** in the domain 0 <= x <= 2pi. You can compile the program with the g++ compiler.

I quote below a personal portable implementation (in C++) of a classic genetic algorithm (evolutionary algorithm) used to maximize the function **f(x, y) = sin(x) * sin(y)** in the domain 0 <= x, y <= 2pi. You can compile the program with the g++ compiler.

I quote below a personal portable implementation (in C++) of a classic genetic algorithm (evolutionary algorithm) used to maximize the function **f(x) = sin(x)** in the domain 0 <= x <= 2pi. You can compile the program with the g++ compiler.

**f(x) = sin(x)** in the domain 0 <= x <= 2pi. You can compile the program with the g++ compiler.

**f(x) = sin(x)** in the domain 0 <= x <= 2pi. You can compile the program with the g++ compiler.

**Taxonomy**

The Artificial Immune Recognition System belongs to the field of Artificial Immune Systems, and more broadly to the field of Computational Intelligence. It was extended early to the canonical version called the Artificial Immune Recognition System 2 (AIRS2) and provides the basis for extensions such as the Parallel Artificial Immune Recognition System [Watkins2004]. It is related to other Artificial Immune System algorithms such as the Dendritic Cell Algorithm, the Clonal Selection Algorithm, and the Negative Selection Algorithm.

**Taxonomy**

The Self-Organizing Map algorithm belongs to the field of Artificial Neural Networks and Neural Computation. More broadly it belongs to the field of Computational Intelligence. The Self-Organizing Map is an unsupervised neural network that uses a competitive (winner-take-all) learning strategy. It is related to other unsupervised neural networks such as the Adaptive Resonance Theory (ART) method. It is related to other competitive learning neural networks such as the the Neural Gas Algorithm, and the Learning Vector Quantization algorithm, which is a similar algorithm for classification without connections between the neurons. Additionally, SOM is a baseline technique that has inspired many variations and extensions, not limited to the Adaptive-Subspace Self-Organizing Map (ASSOM).

**Taxonomy**

The Genetic Algorithm is an Adaptive Strategy and a Global Optimization technique. It is an Evolutionary Algorithm and belongs to the broader study of Evolutionary Computation. The Genetic Algorithm is a sibling of other Evolutionary Algorithms such as Genetic Programming, Evolution Strategies, Evolutionary Programming, and Learning Classifier Systems. The Genetic Algorithm is a parent of a large number of variant techniques and sub-fields too numerous to list.