Bio-Inspired algorithms are a class of optimization methods which rely on techniques inspired by nature, like social interactions, the human brain, or darwinian evolution.
They have been increasingly used in the last years for solving many real-world tasks that can be formulated as optimization problems. Among their numerous strengths, we can consider:
LibCudaOptimize is a GPU-based open source library that allows you to run state-of-the-art bio-inspired optimization heuristics in parallel to optimize a fitness function, introduce a new optimization algorithm, or easily modify/extend existing ones.
In the first case, the only thing you need to do is to write your fitness to be optimized in C++ or CUDA-C, while in the second and third cases, you can take advantage of the framework offered by the library to avoid the need to go deep into basic implementation issues, especially regarding parallel code.
Methods actually implemented in LibCudaOptimize are:
We are currently working on Multi-Objective Automatic Parameter Tuning for bio-inspired optimization algorithms. The code is available here
For more details, see the Publications page.