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”
Abstract: Skepticism of the building block hypothesis has previously been expressed on account of the weak theoretical foundations of this hypothesis and anomalies in the empirical record of the simple genetic algorithm. In this paper we focus on a more fundamental cause for skepticism—the extraordinary strength of some of the assumptions undergirding the building block hypothesis. As many of these assumptions have been embraced by the designers of so called “competent” genetic algorithms, our critique is relevant to an appraisal of such algorithms. We argue that these assumptions are too strong to be acceptable without additional evidence. We then point out weaknesses in the arguments that have been provided in lieu of such evidence.
The conclusion of a manuscript that I recently submitted for review
The biosphere is replete with organisms that are exquisitely well adapted to the environmental niches they inhabit. Natural sexual evolution has been crucial to the generation of what are arguably the most highly adapted of these organisms — cheetahs, owls, humans etc. A deeply intriguing idea is that we can build adaptation algorithms which, at an abstract level, mimic the behavior of natural sexual evolution, and in doing so, “harness” something of the adaptive power of this incredibly effective process. But what is the abstract level at which natural sexual evolution should be mimicked? In other words Continue reading “The Need for a Sound Theory of Adaptation for the Simple Genetic Algorithm”