Sustainable Cooperative Coevolution with a Multi-Armed Bandit

TL;DR

This paper introduces a dynamic multi-armed bandit approach for adaptive resource allocation in cooperative coevolution, leading to faster and more effective solutions for complex problems.

Contribution

It presents a novel self-adaptation mechanism using a dynamic multi-armed bandit to manage species evolution pace in cooperative coevolutionary algorithms.

Findings

Faster solution identification on benchmark and real-world problems

Improved capacity to solve complex problems

Effective resource management through dynamic decision-making

Abstract

This paper proposes a self-adaptation mechanism to manage the resources allocated to the different species comprising a cooperative coevolutionary algorithm. The proposed approach relies on a dynamic extension to the well-known multi-armed bandit framework. At each iteration, the dynamic multi-armed bandit makes a decision on which species to evolve for a generation, using the history of progress made by the different species to guide the decisions. We show experimentally, on a benchmark and a real-world problem, that evolving the different populations at different paces allows not only to identify solutions more rapidly, but also improves the capacity of cooperative coevolution to solve more complex problems.