The Self-Organization of Interaction Networks for Nature-Inspired Optimization

TL;DR

This paper introduces a novel approach to nature-inspired optimization by incorporating biological system properties like self-organized locality and interaction epistasis, leading to enhanced genetic diversity and robustness.

Contribution

It presents the first integration of structural biological properties into evolutionary algorithms, demonstrating emergent diversity and coexistence behaviors.

Findings

Enhanced genetic diversity in populations

Emergent coexistence of distinct individuals

Behavior surpassing canonical and structured EAs

Abstract

Over the last decade, significant progress has been made in understanding complex biological systems, however there have been few attempts at incorporating this knowledge into nature inspired optimization algorithms. In this paper, we present a first attempt at incorporating some of the basic structural properties of complex biological systems which are believed to be necessary preconditions for system qualities such as robustness. In particular, we focus on two important conditions missing in Evolutionary Algorithm populations; a self-organized definition of locality and interaction epistasis. We demonstrate that these two features, when combined, provide algorithm behaviors not observed in the canonical Evolutionary Algorithm or in Evolutionary Algorithms with structured populations such as the Cellular Genetic Algorithm. The most noticeable change in algorithm behavior is an unprecedented capacity for sustainable coexistence of genetically distinct individuals within a single population. This capacity for sustained genetic diversity is not imposed on the population but instead emerges as a natural consequence of the dynamics of the system.