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.

## Archive for February, 2011

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.

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.

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.

**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.

**Taxonomy**

Iterated Local Search is a Metaheuristic and a Global Optimization technique. It is an extension of Multi Start Search and may be considered a parent of many two-phase search approaches such as the Greedy Randomized Adaptive Search Procedure and Variable Neighborhood Search.

**Taxonomy**

Random search belongs to the fields of Stochastic Optimization and Global Optimization. Random search is a direct search method as it does not require derivatives to search a continuous domain. This base approach is related to techniques that provide small improvements such as Directed Random Search, and Adaptive Random Search.

**Taxonomy**

The Adaptive Random Search algorithm belongs to the general set of approaches known as Stochastic Optimization and Global Optimization. It is a direct search method in that it does not require derivatives to navigate the search space. Adaptive Random Search is an extension of the Random Search and Localized Random Search algorithms.

**Taxonomy**

The Stochastic Hill Climbing algorithm is a Stochastic Optimization algorithm and is a Local Optimization algorithm (contrasted to Global Optimization). It is a direct search technique, as it does not require derivatives of the search space. Stochastic Hill Climbing is an extension of deterministic hill climbing algorithms such as Simple Hill Climbing (first-best neighbor), Steepest-Ascent Hill Climbing (best neighbor), and a parent of approaches such as Parallel Hill Climbing and Random-Restart Hill Climbing.

Within the framework of the course “Software Engineering II – Laboratory” (Department of Informatics and Communications, T.E.I. of Central Macedonia) we developed as a semester assignment an application for the management of hotel businesses.

Both the graphical user interface (GUI) and the application were implemented with Qt while the database in SQLite. More specifically, the end user can perform through this application functions such as: processing of customers / rooms, booking rooms, search rooms / customers, inventory of hotel services offered to customers, central management for the correct operation of the hotel, central management of the cost of services provided, as well as adding new users and system administrators to the system.