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).

## Tag Archive: genetic

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.

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 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 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.