A Pyramidal Evolutionary Algorithm with Different Inter-Agent Partnering Strategies for Scheduling Problems

TL;DR

This paper introduces a hierarchical genetic algorithm with various inter-agent partnering strategies for scheduling problems, demonstrating that problem-specific partnering improves solution quality and robustness.

Contribution

It presents a novel pyramidal evolutionary framework combining hierarchical sub-populations with diverse partnering strategies for enhanced scheduling optimization.

Findings

Problem-specific partnering strategies outperform generic ones.

Hierarchical structure enables efficient exploration of large search spaces.

Partnering strategies mitigate issues with inaccurate fitness measurements.

Abstract

This paper combines the idea of a hierarchical distributed genetic algorithm with different inter-agent partnering strategies. Cascading clusters of sub-populations are built from bottom up, with higher-level sub-populations optimising larger parts of the problem. Hence higher-level sub-populations search a larger search space with a lower resolution whilst lower-level sub-populations search a smaller search space with a higher resolution. The effects of different partner selection schemes amongst the agents on solution quality are examined for two multiple-choice optimisation problems. It is shown that partnering strategies that exploit problem-specific knowledge are superior and can counter inappropriate (sub-) fitness measurements.