Death and Progress: How Evolvability is Influenced by Intrinsic Mortality

Abstract

Many factors influence the evolvability of populations, and this article illustrates how intrinsic mortality (death induced through internal factors) in an evolving population contributes favorably to evolvability on a fixed deceptive fitness landscape. We test for evolvability using the hierarchical if-and-only-if (h-iff) function as a deceptive fitness landscape together with a steady state genetic algorithm (SSGA) with a variable mutation rate and indiscriminate intrinsic mortality rate. The mutation rate and the intrinsic mortality rate display a relationship for finding the global maximum. This relationship was also found when implementing the same deceptive fitness landscape in a spatial model consisting of an evolving population. We also compared the performance of the optimal mutation and mortality rate with a state-of-the-art evolutionary algorithm called age-fitness Pareto optimization (AFPO) and show how the two approaches traverse the h-iff landscape differently. Our results indicate that the intrinsic mortality rate and mutation rate induce random genetic drift that allows a population to efficiently traverse a deceptive fitness landscape. This article gives an overview of how intrinsic mortality influences the evolvability of a population. It thereby supports the premise that programmed death of individuals could have a beneficial effect on the evolvability of the entire population.

Selecting for Selection: Learning To Balance Adaptive and Diversifying Pressures in Evolutionary Search

“…Experiments compare novel selection functions learned through Sel4Sel against baseline selection functions from literature that have been explicitly designed to encourage both fitness-based adaptation and diversification. Importantly, evolvability requires both of these pressures, yet surprisingly most quantitative studies of evolvability in artificial life focus on either evolvability as adaptation (Medvet et al, 2017;Veenstra et al, 2020) or evolvability as diversification (Mengistu et al, 2016;Gajewski et al, 2019), but not both. Baseline comparisons are chosen to represent various methods of encouraging evolvability, without explicitly requiring both adaptation and diversification, so as to remain agnostic to the ideal balance between the two.…”

“…This is repeated 20 times for each trial, with the results averaged. In all trials, the population size of the genetic algorithm is 50, and evolution runs for 2000 generations, following parameter choices from Veenstra et al (2020).…”

On the Entanglement between Evolvability and Fitness: an Experimental Study on Voxel-based Soft Robots

“…It is important to note two distinct components of this definition: that there is variation (i.e., diversity) being passed from parent to offspring, and that this variation leads to positive effects on fitness. Interestingly and importantly, measures and studies from artificial life (a primary domain of interest for evolvability studies related to artificial evolution) regard evolvability purely as adaptation (Medvet et al, 2017;Veenstra et al, 2020;Liu et al, 2022;Tarapore and Mouret, 2015), or evolvability as diversification (Mengistu et al, 2016;Gajewski et al, 2019;Stanley, 2011b, 2013;Lim et al, 2021;Carlo et al, 2021), but not both.…”

The Impact of Early-death on Phenotypically Plastic Robots that Evolve in Changing Environments

“…An alternative explanation for aging is programmed death, and it has been criticized by (Kowald and Kirkwood, 2016) while defended by (Goldsmith, 2014). Whereas there seems to be no clear consensus in biology about the existence of programmed death, its benefits to escape deception when optimizing the hierarchical if-and-only-if (H-IFF) function was demonstrated by (Veenstra et al, 2018(Veenstra et al, , 2020. Furthermore, the effect of extinction was studied by (Lehman and Miikkulainen, 2015) in the field of evolutionary robotics, demonstrating that extinction can acceler-ate evolution and increase evolvability.…”