On the Application of Hierarchical Coevolutionary Genetic Algorithms: Recombination and Evaluation Partners

TL;DR

This paper explores hierarchical coevolutionary genetic algorithms with various partner selection strategies, demonstrating that problem-specific partnering improves solution quality in constrained optimization problems.

Contribution

It introduces and evaluates different partnering strategies within hierarchical coevolutionary genetic algorithms, highlighting the benefits of problem-specific knowledge.

Findings

Problem-specific partnering strategies outperform generic ones.

Hierarchical structure allows larger search space exploration with lower resolution.

Effective partner selection can mitigate issues with fitness measurement.

Abstract

This paper examines the use of a hierarchical coevolutionary genetic algorithm under different partnering strategies. Cascading clusters of sub-populations are built from the bottom up, with higher-level sub-populations optimising larger parts of the problem. Hence higher-level sub-populations potentially 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 sub-populations on solution quality are examined for two constrained optimisation problems. We examine a number of recombination partnering strategies in the construction of higher-level individuals and a number of related schemes for evaluating sub-solutions. It is shown that partnering strategies that exploit problem-specific knowledge are superior and can counter inappropriate (sub)fitness measurements.