A multipurpose parallel genetic hybrid algorithm for non-linear non-convex programming problems

Abstract

The genetic hybrid algorithm (GHA) is a general-purpose algorithm, spanning several areas of mathematical problem solving. GHA makes calls to an (empty) accelerator function at key stages of the solution process, providing it with the current population of solution vectors in the argument list of the function. On return from the accelerator, GHA processes the population further. The user has control over the specific stage (generation of a new population, crossover, mutation etc.) and can modify the population of solution vectors, e.g., by making calls to special purpose algorithms through the accelerator channel. If needed, the steps of GHA can be partly or completely superseded by the special purpose mathematical/artificial intelligence based algorithm. The system can be used as a package for classical mathematical programming with the genetic sub-block deactivated. On the other hand, the algorithm can be turned into a machinery for stochastic analysis (e.g. for Monte Carlo simulation, time series modelling or neural networks), where the mathematical programming and genetic computing facilities are deactivated. Finally, pure evolutionary computation can be activated for studying genetic phenomena. As a completely new feature, we design and implement a flexible multicomputer framework for the basic GHA.