BUGS : A Bug-Based Search Strategy Using Genetic Algorithms

Abstract

We present a new approach to problem solving using Genetic Algorithms (GA). Earlier GA did not contain local search (i.e. hill climbing) mechanisms, which led to optimization difficulties, especially in higher dimensions. To overcome such difficulties, we introduce a "bug-based" search strategy and implement a system called BUGS. The ideas behind this are derived from biologically realistic foraing behaviors of bugs or animals. The effectiveness of this approach is confirmed using optimization experiments, and computer vision applications.