PDoublePop: An implementation of parallel genetic algorithm for function optimization

Published: 08-03-2017| Version 1 | DOI: 10.17632/shf4yvshm2.1
Ioannis Tsoulos,
Alexandros Tzallas,
Dimitris Tsalikakis


A software for the implementation of parallel genetic algorithms is presented in this article. The underlying genetic algorithm is aimed to locate the global minimum of a multidimensional function inside a rectangular hyperbox. The proposed software named PDoublePop implements a client–server model for parallel genetic algorithms with advanced features for the local genetic algorithms such as: an enhanced stopping rule, an advanced mutation scheme and periodical application of a local search procedure. The user may code the objective function either in C++ or in Fortran77. The method is tested on a series of well-known test functions and the results are reported.