Theoretical Convergence Guarantees for Cooperative Coevolutionary Algorithms
Abstract
Cooperative coevolutionary algorithms have the potential to significantly speed up
the search process by dividing the space into parts that can be each conquered
separately. Unfortunately, recent research presented theoretical and empirical
arguments that these algorithms might not be fit for optimization tasks, as they
might tend to drift to suboptimal solutions in the search space. This paper details
an extended formal model for cooperative coevolutionary algorithms, and uses it to
demonstrate that these algorithms will converge to the globally optimal solution,
if properly set and if given enough resources. We also present an intuitive
graphical visualization for the basins of attraction to optimal and suboptimal
solutions in the search space.
