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Advertisement Hide. Intelligence and Evolutionary Biology. Conference proceedings. Papers Table of contents 24 papers About About these proceedings Table of contents Search within book. Pages Intelligence and Natural Selection. Artificial Intelligence and Biological Intelligence.
Comparative Neuroanatomy and the Evolution of Intelligence. The Forebrain as a Playground of Mammalian Evolution. Mutations ensure diversity, which itself is a hedge that populations make against disease. Mutations also support the continued exploration of a large combinatorial and undifferentiable problem, which is especially important given that environments change, 1 and those changes can kill off a stagnant and homogeneous species, or favor a novel mutation in its ranks.
Genetic variation emerges due to damaged DNA, transposition, errors in DNA replication, broken DNA repair processes and recombination; in algorithms, it results from deliberate point mutations in parameters e.
Genetic and evolutionary algorithms apply the above ideas to mathematical functions. You could say that a genetic algorithm is like a species. It spawns many singular and unique variations of itself, and those variations are like moth children doomed to be tested against the rigors of the environment. While the environment in real life tests many things about an organism — strength, intelligence, emotional IQ, fashion sense — with algorithms, we usually have a single measure of performance: how well an individual instance does with a so-called fitness function.
Fitness is a measure of how well an algorithm performs against its predictive goal. Your input is the number of square feet in the house and the output is a dollar amount, the slope of your line is a and the y-axis intercept is b:.
Fitness, in this case, minimizes the distance between the data points and the line. Some lines will cut through the scatter plot very far from most of the points imagine a vertical line through one edge of the dots.
The line you see slices the cloud of dots through the center, minimizing the aggregate distance of all dots from the closest points on the line. Since many problems are too complicated to eyeball, you want to computationally seek the best function.
Evolutionary computation - Wikipedia
A genetic algorithm would begin by randomly generating a group of linear regression functions, with slopes and intercepts that are clearly unsuited to the data at hand. Those randomly generated lines are then measured against the data to calculate their total error.
Using the same mathematical Researchers Unearth 'New' Mass-Extinction. The seas began to ice over at the Earth's poles, and new species evolved with the new temperatures. The cause of Most Popular Stories.
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