  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.
