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An Introduction to Genetic Algorithms (Complex Adaptive Systems)

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Science arises from the very human desire to understand and control the world. Over the course of history, we
humans have gradually built up a grand edifice of knowledge that enables us to predict, to varying extents, the
weather, the motions of the planets, solar and lunar eclipses, the courses of diseases, the rise and fall of
economic growth, the stages of language development in children, and a vast panorama of other natural,
social, and cultural phenomena. More recently we have even come to understand some fundamental limits to
our abilities to predict. Over the eons we have developed increasingly complex means to control many aspects
of our lives and our interactions with nature, and we have learned, often the hard way, the extent to which
other aspects are uncontrollable.

The advent of electronic computers has arguably been the most revolutionary development in the history of
science and technology. This ongoing revolution is profoundly increasing our ability to predict and control
nature in ways that were barely conceived of even half a century ago. For many, the crowning achievements
of this revolution will be the creation—in the form of computer programs—of new species of intelligent
beings, and even of new forms of life.

The goals of creating artificial intelligence and artificial life can be traced back to the very beginnings of the
computer age. The earliest computer scientists—Alan Turing, John von Neumann, Norbert Wiener, and
others—were motivated in large part by visions of imbuing computer programs with intelligence, with the
life−like ability to self−replicate, and with the adaptive capability to learn and to control their environments.
These early pioneers of computer science were as much interested in biology and psychology as in
electronics, and they looked to natural systems as guiding metaphors for how to achieve their visions. It
should be no surprise, then, that from the earliest days computers were applied not only to calculating missile
trajectories and deciphering military codes but also to modeling the brain, mimicking human learning, and
simulating biological evolution. These biologically motivated computing activities have waxed and waned
over the years, but since the early 1980s they have all undergone a resurgence in the computation research
community. The first has grown into the field of neural networks, the second into machine learning, and the
third into what is now called "evolutionary computation," of which genetic algorithms are the most prominent
example.

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