Genetic algorithms have wonderfully diverse capabilities extending beyond those of Neural Networks. Genetic algorithms can solve simple linear problems and they can also solve much more complex higher dimensional problems, too. These capabilities are not different from Neural Networks, though. The power of genetic algorithms lies in there ability to do problems that rely on recursion and outside data structures. Genetic algorithms can solve recursive sequence like Fibbonnacci and process stacks; things no Neural Network can do. Genetic algorithms can even assume the form of a simple computer program. These capabilities are the most interesting (at least in my opinion). When coupled with an interpreted language like Java or Lisp, they can dynamically generate programs to solve the programmers needs.

Examples of real world genetic algorithm capabilities include

• Voice Recognition (www.newscientist.com)
• Artificial Limbs (www.newscientist.com)
• Stock market forecasting (www.newscientist.com)
• Autonomous Robots (www.ai.mit.edu)
• Intelligent Agents
• and more...

In my project, I create only a simple pattern recognition algorithm, but the framework provided gives a reasonable base for more involved projects. First let's discuss the basic structure of a genetic algorithm.