JGAP is a genetic algorithms package written in Java. It is designed to require minimum effort to use "out of the box", but is also designed to be highly modular. It allows custom components to be easily plugged in. JGAP version 3.0 (Release Candidate 2) represents a major release introducing the long awaited Genetic Programming capabilities to JGAP, including a GP example.
Genetic algorithms are evolutionary algorithms that use the principle of natural selection to evolve a set of solutions toward an optimum solution.
We took the feedback from RC1 to enhance the current release and present it as Release Candidate 2. As always, the javadocs have been improved, of course new unit tests have been added (we are counting over 1100), and a new example demonstrates custom randomized population initialization. The example was created due to a request on the jgap users list (we love getting feedback from you!). This release can be downloaded here.