Sustainable Cooperative Coevolution with a Multi-Armed Bandit
This work addresses resource management in evolutionary computation for researchers, but it is incremental as it extends existing bandit frameworks.
The paper tackles the problem of resource allocation in cooperative coevolutionary algorithms by proposing a self-adaptation mechanism based on a dynamic multi-armed bandit, which experimentally identifies solutions more rapidly and improves problem-solving capacity on benchmarks and real-world problems.
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.