Computer scientists turn to evolutionary biology for inspiration

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Image credit: Glomacs

Creationists love to insist that evolution had to assemble upward of 300 amino acids in the right order to create just one medium-size human protein. With 20 possible amino acids to occupy each of those positions, there would seemingly have been more than 20300 possibilities to sift through, a quantity that renders the number of atoms in the observable universe inconsequential.

Computer scientists face similar challenges with problems that involve finding optimal solutions among astronomically huge sets of possibilities. Some have looked to biology for inspiration — even as biologists still try to figure out exactly how life does it, too.

Genetic algorithms, optimization methods that have been popular for decades, use the principles of natural selection to engineer new designs (for robots, drugs and transportation systems, among other things), train neural networks, or encrypt and decrypt data. The technique starts by treating random solutions to a problem as “organisms” that have certain features or elements “genetically” described in their code. These solutions aren’t particularly good, but they then undergo combinations of random mutations (and sometimes other changes that mimic gene-shuffling processes) to produce a second generation of organisms, which are in turn tested for their “fitness” at performing the desired task. Eventually, many repetitions of this process lead to a highly fit individual, or solution.

Related article:  Blame human evolution for corporate jargon and thick academic prose

The idea of thinking about life as evolving software is fertile.

Read full, original post: Mathematical Simplicity May Drive Evolution’s Speed

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