The Missing Algorithm: Sex, Cooperation, and Prediction in Evolution
Editorial Reviews
Book Description
This book proposes a novel model of gene spread that might solve many puzzles of evolution.
The standard algorithm of evolution is that genes compete only to spread in the greatest number. In truth, most genes in a genome spread in equal numbers across single populations, but that is not the end of all competition. Rather, while genes are competing to spread, each gene also competes to retain its sequence slightly better than a rival, by forcing other genes in the genome to do the altering necessary to adapt, and bear the fitness cost of change. So that while many genes or DNA fragments vary even between family members, other genes can spread across species, classes or even phyla by barely altering. These highly conserved genes gain slight fitness over their more mutable rivals, over all the populations of life.
Well yes, but how does conservation of sequence translate as fitness of total gene numbers?
Well it can translate if conservation is summed along a complex plane (root -1). Nobody has considered this before, but it offers a surprisingly logical model of sex, cooperation, punctuated rates of evolution and many other conundrums.
From the Author
This book will assist researchers seeking a fresh way to make existing algorithmic models work, for sex, cooperation, or evolution of the chromosome. Anybody discovering a few short genes playing vital roles in development, or suspecting that genes can act with foresight, will find a challenging explanation of what might be happening. Researchers in AI or complexity will find a suggested model in which genes compete along two axes of gene freedom, which should better correlate to how real life evolves.
For the general reader, here is a selfish gene model that also offers a logical explanation broader evolution, and even higher life and human behavior.
The Missing Algorithm: Sex, Cooperation, and Prediction in Evolution,Sean Gould,Universal Publishers,1581125992,Life Sciences - Evolution,Science,Science/Mathematics,Evolution
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