Death and Progress: How Evolvability is Influenced by Intrinsic Mortality

Area of Science:

• Evolutionary biology
• Computational intelligence
• Population genetics

Background:

• Evolvability is crucial for adaptation but influenced by many factors.
• Deceptive fitness landscapes pose challenges to evolutionary processes.
• Intrinsic mortality's role in evolvability remains under-explored.

Purpose of the Study:

• To investigate how intrinsic mortality affects population evolvability.
• To identify the relationship between mutation and mortality rates for optimal evolution on deceptive landscapes.
• To compare intrinsic mortality-based evolution with state-of-the-art algorithms.

Main Methods:

• Utilized the hierarchical if-and-only-if (h-iff) function as a deceptive fitness landscape.
• Employed a steady-state genetic algorithm (SSGA) with variable mutation and intrinsic mortality rates.
• Implemented a spatial model and compared results with age-fitness Pareto optimization (AFPO).

Main Results:

• A specific relationship between mutation and intrinsic mortality rates was found to facilitate reaching the global maximum.
• This relationship held true in both the SSGA and the spatial model.
• The intrinsic mortality and mutation rate combination induces random genetic drift, aiding landscape traversal.

Conclusions:

• Intrinsic mortality positively influences population evolvability on deceptive fitness landscapes.
• Programmed death can be a beneficial mechanism for enhancing evolutionary adaptation.
• The interplay of mutation and mortality rates is critical for navigating complex evolutionary challenges.