## A Nature Inspired Sudoku Solver.

This machine learning 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).

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.

**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.

## 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.

## 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.