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Evolution and Complex Structures:

Evolution and Complex Structures:. Simulated Evolution Hints at Features? Eric Duchon March 17, 2008. Complex Structures. Darwin:.

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Evolution and Complex Structures:

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  1. Evolution and Complex Structures: Simulated Evolution Hints at Features? Eric Duchon March 17, 2008

  2. Complex Structures Darwin: To suppose that the eye with all its inimitable contrivances for adjusting the focus to different distances, for admitting different amounts of light, and for the correction of spherical and chromatic aberration, could have been formed by natural selection, seems, I freely confess, absurd in the highest degree. Even today, it is not clear how many of the complex structures in Nature evolved.

  3. The Eye How do genetic mutations create more complex eyes without intermediate steps destroying their advantages?

  4. Arguments For Simulation • Fossil records not complete enough to track emergence of complexity • Lab experiments limited by number of generations and by ability to track mutations through generations • Computer simulations allows exact tracking of mutations • Limited by computer resources and a simplified model

  5. Computer Models Evolutionary simulations are usually modified cellular automata. Although not useful for directly modeling biological systems, they can offer support for suspicions and theories. In particular, work with Avida has elucidated how complexity can arise.

  6. Digital Organisms • The genome is a circular sequence of instructions (26 possible) • Energy: received single instruction processing units (SIPs) relative to the rest of the organisms • Rate of errors when replicating the genome • 0.175: an instruction to be copied is switched for another • 0.05: single instruction is deleted or added • Environment determined by what merited additional SIPs

  7. Competition and Fitness • Competition was introduced by assigning additional computational time to organisms which demonstrated logical functions • The SIPs an organism received was proportional to the product of genome length and computational merit.

  8. Reading a Digital Genome

  9. Locating Complexity • Computational merit was assigned on the basis of complexity of the genome required to produce the logic function. • With the possible instructions, NOT and NAND were the easiest to create while EQU was the most difficult (it required at least 19 instructions). So to investigate complexity, the emergence of the EQU operation was tracked.

  10. Case Study: A genotype with all operations • This genotype achieved all logical operations. Not all the mutations were advantageous, as seen on top right. However, even the deleterious mutation that knocked out the NAND function was essential for forming EQU in the next replication.

  11. Conclusions • Support for Darwin’s general idea that complex structures evolve from simpler ones. • A reasonable demonstration of the usefulness of cellular automata?

  12. More Generally, • Out of 50 populations, 23 gained EQU. • The final genomes ranged from 49 to 356 instructions, so tendency to larger genomes. • Median of seven of eight simpler functions already apparent before EQU. • The mutation to EQU caused 20 of 23 genotypes to lose at least one simpler operation. • But when only EQU was rewarded, no populations evolved that trait.

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