Spontaneous organization leads to robustness in evolutionary algorithms

TL;DR

This paper demonstrates that spontaneous self-organization of population networks in evolutionary algorithms enhances robustness and search performance across various problems, introducing a new paradigm for designing resilient heuristics.

Contribution

It introduces a self-organizing topology mechanism guided by fitness and clustering, improving robustness in evolutionary algorithms compared to traditional methods.

Findings

Self-organizing topologies improve robustness.

Promising performance over multiple problem types.

Co-evolution of population and topology is effective.

Abstract

The interaction networks of biological systems are known to take on several non-random structural properties, some of which are believed to positively influence system robustness. Researchers are only starting to understand how these structural properties emerge, however suggested roles for component fitness and community development (modularity) have attracted interest from the scientific community. In this study, we apply some of these concepts to an evolutionary algorithm and spontaneously organize its population using information that the population receives as it moves over a fitness landscape. More precisely, we employ fitness and clustering based driving forces for guiding network structural dynamics, which in turn are controlled by the population dynamics of an evolutionary algorithm. To evaluate the effect this has on evolution, experiments are conducted on six engineering design problems and six artificial test functions and compared against cellular genetic algorithms and 16 other evolutionary algorithm designs. Our results indicate that a self-organizing topology evolutionary algorithm exhibits surprisingly robust search behavior with promising performance observed over short and long time scales. After a careful analysis of these results, we conclude that the coevolution between a population and its topology represents a powerful new paradigm for designing robust search heuristics.