This is the Geneva optimization library. As the name suggests
-- it stands for "Grid-enabled evolutionary algorithms" --, it
aims to provide the necessary tools to perform parametric optimization
in parallel on devices ranging from multi-processor machines over
clusters all the way to Grid- and Cloud-installations.

Meanwhile, further optimization algorithms have been added beyond "mere"
evolutionary algorithms, such as particle swarm optimization, simulated
annealing, a simple form of gradient descents and an exhaustive parameter scan.
Further algorithms will be added over time, and the existing algorithms will be
further improved.

All algorithms act on the same problem definitions, and different 
optimization algorithms can interact and may be "chained", making it 
easy to tackle even complicated problems. Parallelization (on the level
of optimization algorithms) happens mostly transparently for the user. 

Geneva does not want to be the fastest library of optimization
algorithms on small machines, but is instead targeted at
large scale problems, where the evaluation of a single individual will
typically take longer than a few seconds.

Geneva is made available as an Open Source software, under the Apache
License, version 2.0. External contributions to the code are very
welcome, and indeed we make this code available in the hope that it
will not only be useful, but will also encourage users to let us know
about bugs or suggest or even implement features they would want to
see in Geneva.