Archive
The project ‘PGASystem’ (Parallel Genetic Algorithms System).
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.
Automatic Software Repairing with Genetic Programming.
Efstathios Chatzikyriakidis, 07/05/11
Introduction
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.
Ant Colony System (Swarm Algorithm).
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.
Cultural Algorithm (Physical Algorithm).
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.
Harmony Search (Physical Algorithm).
Taxonomy
Harmony Search belongs to the fields of Computational Intelligence and Metaheuristics.
Simulated Annealing (Physical Algorithm).
Taxonomy
Simulated Annealing is a global optimization algorithm that belongs to the field of Stochastic Optimization and Metaheuristics. Simulated Annealing is an adaptation of the Metropolis-Hastings Monte Carlo algorithm and is used in function optimization. Like the Genetic Algorithm, it provides a basis for a large variety of extensions and specialization’s of the general method not limited to Parallel Simulated Annealing, Fast Simulated Annealing, and Adaptive Simulated Annealing.
Memetic Algorithm (Physical Algorithm).
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.
Presentation at T.E.I of Serres on Genetic Routing.
Within the framework of the course “Computer Networks III – Theory” (Department of Informatics and Communications, T.E.I of Serres) we were asked to write a presentation related to the content of the course. The topic of my presentation was the Genetic Routing.
Differential Evolution – Example 5.
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.
Genetic Algorithm – Example 4.
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.
Genetic Algorithm – Example 3.
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.
Genetic Algorithm – Example 2.
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.
Genetic Algorithm – Example 1.
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.
Artificial Immune Recognition System (Immune Algorithm).
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.
Self-Organizing Map (Neural 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).
Genetic Algorithm (Evolutionary Algorithm).
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.
Iterated Local Search (Stochastic Algorithm).
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.
Random Search (Stochastic Algorithm).
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.
Adaptive Random Search (Stochastic Algorithm).
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.
Stochastic Hill Climbing (Stochastic Algorithm).
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.
Arduino: Remote control of lamp through double beat of the palm.
This project refers to an Arduino sketch which with the help of a relay (electromechanical switch), the system VRBot and the expansion card for sensors (Sensor Shield) can control (enable or disable) an external device (eg. a lamp, a car alarm, a home air conditioner, etc.) with a double beat of the palm.
Arduino: Executing applications with voice command recognition.
This project refers to an Arduino sketch which with the help of a VRBot system can recognize voice commands, making it possible to run applications. For example, we could cause the execution of the relevant browser with the voice command «Firefox». Also included in this project is a Python script that implements a background service (Daemon) which communicates with the Arduino and initiates the execution of various applications.
Arduino: Adjustable mood lighting with voice recognition.
This project refers to an Arduino sketch which, with the help of an RGB LED and a VRBot system, can implement a mood lighting system controlled by voice commands. There are voice commands to activate / deactivate the red, green and blue. The color changes are performed with fade effects.
Arduino: Security Access Control with Multi-Factor Authentication.
This project refers to an Arduino sketch which – with the help of a system VRBot, a basic keyboard, an RFID reader and a piezo speaker – can implement a Security Access Control with Multi-Factor Authentication.
The system comprises a security level associated with typing a password (something I know), a security level associated with the use of an RFID tag (something I have) and a security level associated with the recognition of a voice message password (something I am).
Arduino: Device Control (open or close) via a relay and voice recognition.
This project refers to an Arduino sketch which with the help of a relay (electromechanical switch) and the VRBot system can drive an external device (eg. a household lamp, a car alarm, a home air conditioner etc.) with voice commands. The system VRBot is properly trained with voice messages and then performs voice recognition.
More specifically, the Arduino detects acoustic sounds and tries to recognize them through the system VRBot. Finally, if acceptable voice messages are identified, the Arduino manages them for the use of the device driven with the help of the relay.
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