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Death and Progress: How Evolvability is Influenced by Intrinsic Mortality.

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Intrinsic mortality, or programmed death, enhances a population's evolvability on complex fitness landscapes. This study reveals a key relationship between mutation and mortality rates for optimal evolution.

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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.