Ring Migration Topology Helps Bypassing Local Optima

TL;DR

This paper demonstrates that ring migration topology in island models effectively maintains diversity and outperforms complete topology in escaping local optima, achieving optimal black box complexity for a specific fitness function.

Contribution

It introduces the FORK fitness function and shows how ring topology with rare migrations achieves optimal runtime, surpassing complete topology in diversity preservation.

Findings

Ring topology achieves black box complexity of (n^r) for FORK.

Complete topology reaches (n^{1.5r}) runtime, less efficient.

Ring topology maintains diversity better than complete topology.

Abstract

Running several evolutionary algorithms in parallel and occasionally exchanging good solutions is referred to as island models. The idea is that the independence of the different islands leads to diversity, thus possibly exploring the search space better. Many theoretical analyses so far have found a complete (or sufficiently quickly expanding) topology as underlying migration graph most efficient for optimization, even though a quick dissemination of individuals leads to a loss of diversity. We suggest a simple fitness function FORK with two local optima parametrized by $r \geq 2$ and a scheme for composite fitness functions. We show that, while the (1+1) EA gets stuck in a bad local optimum and incurs a run time of $\Theta(n^{2r})$ fitness evaluations on FORK, island models with a complete topology can achieve a run time of $\Theta(n^{1.5r})$ by making use of rare migrations in order to explore the search space more effectively. Finally, the ring topology, making use of rare migrations and a large diameter, can achieve a run time of $\tilde{\Theta}(n^r)$, the black box complexity of FORK. This shows that the ring topology can be preferable over the complete topology in order to maintain diversity.