Steven Pinker -- How the Mind Works - Hampshire High Italian ...

Revenge of the Nerds 177in a computer program, and the selection merely preserve the systemsthat do not crash? A new field of computer science called genetic algorithmshas shown that Darwinian selection can create increasingly intelligentsoftware. Genetic algorithms are programs that are duplicated tomake multiple copies, though with random mutations that make eachone a tiny bit different. All the copies have a go at solving a problem, andthe ones that do best are allowed to reproduce to furnish the copies forthe next round. But first, parts of each program are randomly mutatedagain, and pairs of programs have sex: each is split in two, and the halvesare exchanged. After many cycles of computation, selection, mutation,and reproduction, the surviving programs are often better than anythinga human programmer could have designed.More apropos of how a mind can evolve, genetic algorithms havebeen applied to neural networks. A network might be given inputs fromsimulated sense organs and outputs to simulated legs and placed in a virtualenvironment with scattered "food" and many other networks competingfor it. The ones that get the most food leave the most copiesbefore the next round of mutation and selection. The mutations are randomchanges in the connection weights, sometimes followed by sexualrecombination between networks (swapping some of their connectionweights). During the early iterations, the "animals"—or, as they aresometimes called, "animats"—wander randomly over the terrain, occasionallybumping into a food source. But as they evolve they come to zipdirectly from food source to food source. Indeed, a population of networksthat is allowed to evolve innate connection weights often does betterthan a single neural network that is allowed to learn them. That isespecially true for networks with multiple hidden layers, which complexanimals, especially humans, surely have. If a network can only learn, notevolve, the environmental teaching signal gets diluted as it is propagatedbackward to the hidden layers and can only nudge the connectionweights up and down by minuscule amounts. But if a population of networkscan evolve, even if they cannot learn, mutations and recombinationscan reprogram the hidden layers directly, and can catapult thenetwork into a combination of innate connections that is much closer tothe optimum. Innate structure is selected for.Evolution and learning can also go on simultaneously, with innatestructure evolving in an animal that also learns. A population of networkscan be equipped with a generic learning algorithm and can be allowed toevolve the innate parts, which the network designer would ordinarily