Optimizing Floating-Point Problems with Evolutionary Algorithms

[This is an extended abstract for a talk I'm planning]

Evolutionary Algorithms (EA) are a superset of genetic algorithms and related optimization algorithms. Genetic algorithms usually work with bits and small integer numbers.

There are other EAs that directly work with floating point numbers, among the Differential Evolution (DE) [1] [2] [3].

The talk gives an introduction to optimization of floating-point problems with DE. It uses examples from electrical engineering as well as from optimization of actuation waveforms of inkjet printers. Piezo-electric inkjet printers use an actuation waveform to jet drops out of a nozzle. This waveform (among other parameters like the jetted fluid) determines the quality of the jetted drops.

For the software I'm using the Python bindings PGApy [4] for the Parallel Genetic Algorithm Package PGAPack [5] which was originally developed at Argonne National Laboratories. I'm maintaining both packages for some years now. Among others, Differential Evolution (DE) and strategies for multi-objective optimization (using NSGA-II [6]) where newly implemented.