AI::GA implements a (hopefully) generalized genetic algorithm. It does this by using an array of allowed tokens as individuals. The user has to provide a fitness function. There, the actual representation is implemented. If you have a string of chars, you can simply join them. If you want to have real numbers, you should probably use a bitwise representation and calculate the real values in your fitness function.
The OpenAI project is centered around the advancement of Artificial Intelligence. It is geared toward developing specifications for AI and a default implementation for a set of well-known AI tools. The genetic algorithm is written in Java and is built in a modular fashion so that new algorithms and evolution rules can be created.
OpenAI is a project which is centred around the advancement of Artificial Intelligence. The project itself is geared toward developing specifications for AI and a default implementation for a set of well known AI tools. This is OpenAI's Neural Network release. The software is written in Java and is built in a modular fashion so that new algorithms and learning rules can be created. Configuration and persistence will be done through XML and a CORBA interface is provided for applications that wish to incorporate the technology.
Jude is a rapid application development tool to develop data management workgroup applications that easy-to-maintain for developers and easy-to-use for end users. It is based on a knowledge base with an object oriented structure on the server side and a compound document agent-based user interface on the client side.
ImSafe (Immune Security For your Enterprise) is a host-based intrusion detection tool. After a learning phase, it is able to detect changes in processes behavior, to detect buffer overflows, etc. It is implemented through a device driver (as a kernel patch) for the Linux kernel, but can also be run on other UNIX systems by using a "sensor" built upon strace.
Weka is a collection of machine learning algorithms for solving real-world data mining problems. It is written in Java and runs on almost any platform. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka is also well-suited for developing new machine learning schemes. The development version contains a GUI with visualization tools and direct database access.