From the preface to my dissertation:

The foundations of most computer engineering disciplines are almost entirely mathematical. There is, for instance, almost no question about the  soundness of the foundations of such engineering disciplines as graphics, machine learning, programming languages, and databases. An exception to this general rule is the field of genetic algorithmics, whose foundation includes a significant scientific component.

The existence of a science at the heart of this computer engineering discipline is  regarded with nervousness. Science traffics in provisional truth; it requires one to adopt a form of skepticism that is more nuanced, and hence more difficult to master than the radical sort of skepticism that suffices in mathematics and theoretical computer science. Many, therefore, would be happy to see science excised from the foundations of genetic algorithmics. Indeed, over the past decade and a half, much effort seems to have been devoted to turning genetic algorithmics into just another field of computer engineering, one with an entirely mathematical foundation.

Broadening one’s perspective beyond computer engineering, however, one cannot help wondering if much of this effort is not a little misplaced. Continue reading “Back to the Future: A Science of Genetic Algorithms” →