Intrinsic Mortality Governs Evolvability

Frank Veenstra, Pablo González de Prado Salas, Josh Bongard, Kasper Stoy, Sebastian Risi

Research output: Conference Article in Proceeding or Book/Report chapterArticle in proceedingsResearchpeer-review


An ongoing discussion in biology concerns whether intrinsic
mortality, or senescence, is programmed or not. The death
(i.e. removal) of an individual solution is an inherent feature
in evolutionary algorithms that can potentially explain how
intrinsic mortality can be beneficial in natural systems. This
paper investigates the relationship between mutation rate and
mortality rate with a steady state genetic algorithm that has a
specific intrinsic mortality rate. Experiments were performed
on a predefined deceptive fitness landscape, the hierarchical
if-and-only-if function (H-IFF). To test whether the relationship
between mutation and mortality rate holds for more complex
systems, an agent-based spatial grid model based on the
H-IFF function was also investigated. This paper shows that
there is a direct correlation between the evolvability of a population
and an indiscriminate intrinsic mortality rate to mutation
rate ratio. Increased intrinsic mortality or increased mutation
rate can cause a random drift that can allow a population
to find a global optimum. Thus, mortality in evolutionary
algorithms does not only explain evolvability, but might also
improve existing algorithms for deceptive/rugged landscapes.
Since an intrinsic mortality rate increases the evolvability of
our spatial model, we bolster the claim that intrinsic mortality
can be beneficial for the evolvability of a population.
Original languageEnglish
Title of host publicationArtificial Life Conference Proceedings : ALIFE 2018
Number of pages8
PublisherMIT Press
Publication date2018
Publication statusPublished - 2018


  • Intrinsic Mortality
  • Senescence
  • Evolutionary Algorithms
  • Mutation Rate
  • Evolvability
  • Intrinsic Mortality Rate
  • Steady State Genetic Algorithm
  • Deceptive Fitness Landscape
  • Hierarchical If-And-Only-If Function (H-IFF)
  • Agent-Based Spatial Grid Model


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