Geometric firefly algorithms on graphical processing units

Abstract

Geometric unification of Evolutionary Algorithms (EAs) has resulted in an expanding set of algorithms which are search space invariant. This is important since search spaces are not always parametric. Of particular interest are combinatorial spaces such as those of programs that are searchable by parametric optimisers, providing they have been specially adapted in this way. This typically involves redefining concepts of distance, crossover and mutation operators. We present an informally modified Geometric Firefly Algorithm for searching expression tree space, and accelerate the computation using Graphical Processing Units. We also evaluate algorithm efficiency against a geometric version of the Genetic Programming algorithm with tournament selection. We present some rendering techniques for visualising the program problem space and therefore to aid in characterising algorithm behaviour. © 2014 Springer International Publishing Switzerland.