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.
Keywords: Intrinsic mortality; evolutionary computation; evolvability; senescence; spatial models.