Abstract
Evolution is intrinsically algorithmic in nature. Because of this, it is possible to describe the evolutionary process in computational terms. Such a description constitutes a generalized theory of evolution that applies, at the very least, to the evolution of DNA and to evolution in silico. Computational theory provides a formal structure that allows measure of single object complexity. Single object characterizations differ from measures suggested by the statistical methods of physics and communication theory. Computational theory also provides the means for including time in measures of complexity. Measures of object complexity that incorporate the minimal time of origin will be introduced, and also an argument that certain evolutionary processes tend to produce entities (objects) with ever-greater complexity if the measure of complexity obeys a "slow growth" condition. These arguments suggest that a consequence of the evolutionary process is a strong tendency to accumulate complexity measured by minimal history.