Death in Genetic Algorithms

TL;DR

This paper investigates the role of death and aging in genetic algorithms, demonstrating that incorporating death can improve efficiency, solution quality, and robustness by helping algorithms escape local optima.

Contribution

It provides experimental evidence that death as an adaptive trait enhances genetic algorithms' performance and offers insights into how aging theories can be applied to evolutionary computation.

Findings

Senescent death reduces run-time of GAs

Death increases solution optimality

Death decreases performance variance

Abstract

Death has long been overlooked in evolutionary algorithms. Recent research has shown that death (when applied properly) can benefit the overall fitness of a population and can outperform sub-sections of a population that are "immortal" when allowed to evolve together in an environment [1]. In this paper, we strive to experimentally determine whether death is an adapted trait and whether this adaptation can be used to enhance our implementations of conventional genetic algorithms. Using some of the most widely accepted evolutionary death and aging theories, we observed that senescent death (in various forms) can lower the total run-time of genetic algorithms, increase the optimality of a solution, and decrease the variance in an algorithm's performance. We believe that death-enhanced genetic algorithms can accomplish this through their unique ability to backtrack out of and/or avoid getting trapped in local optima altogether.