Partnering Strategies for Fitness Evaluation in a Pyramidal Evolutionary Algorithm

TL;DR

This paper investigates hierarchical distributed genetic algorithms with various partnering strategies, finding that random partner selection enhances diversity and sampling, thereby improving optimization performance.

Contribution

It introduces a pyramidal evolutionary algorithm framework and evaluates different partner selection schemes, highlighting the effectiveness of random partnering strategies.

Findings

Random partnering yields better diversity and sampling.

Hierarchical sub-populations optimize larger problem parts.

Lower-level sub-populations focus on high-resolution search.

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 for (sub-)fitness evaluation purposes are examined for two multiple-choice optimisation problems. It is shown that random partnering strategies perform best by providing better sampling and more diversity.