# General Subpopulation Framework and Taming the Conflict Inside Populations

## TL;DR

This paper introduces a general subpopulation framework for evolutionary algorithms, enhancing multi-objective optimization by integrating diverse strategies, improving results, and reducing internal conflicts within populations.

## Contribution

It formalizes a versatile subpopulation framework that generalizes many structured evolutionary algorithms and demonstrates its effectiveness with new algorithms that outperform traditional methods.

## Key findings

- Subpopulation algorithms outperform panmictic counterparts.
- Adding single-objective differential evolution improves multi-objective results.
- Framework reduces deleterious internal competition within populations.

## Abstract

Structured evolutionary algorithms have been investigated for some time. However, they have been under-explored specially in the field of multi-objective optimization. Despite their good results, the use of complex dynamics and structures make their understanding and adoption rate low. Here, we propose the general subpopulation framework that has the capability of integrating optimization algorithms without restrictions as well as aid the design of structured algorithms. The proposed framework is capable of generalizing most of the structured evolutionary algorithms, such as cellular algorithms, island models, spatial predator-prey and restricted mating based algorithms under its formalization. Moreover, we propose two algorithms based on the general subpopulation framework, demonstrating that with the simple addition of a number of single-objective differential evolution algorithms for each objective the results improve greatly, even when the combined algorithms behave poorly when evaluated alone at the tests. Most importantly, the comparison between the subpopulation algorithms and their related panmictic algorithms suggests that the competition between different strategies inside one population can have deleterious consequences for an algorithm and reveal a strong benefit of using the subpopulation framework. The code for SAN, the proposed multi-objective algorithm which has the current best results in the hardest benchmark, is available at the following https://github.com/zweifel/zweifel