JOHN E. MAYFIELD, Professor
Ph.D. Pittsburgh, 1968
e-mail: jemayf@iastate.edu
The goal of my research is to develop a generalized theory of evolution that relates naturally to fundamental mathematical and physical concepts of complexity.
Description of research program: I have come to accept that evolution should not be seen not so much as a biological phenomenon as a computational one. Various processes including biological evolution, plant and animal breeding, antibody maturation, and evolutionary and genetic algorithms are all based on an information processing strategy that is effectively described as iterated probabilistic computation (Figure).
Figure.
Diagram of iterated probabilistic computations with selection (IPCS). This simple computational strategy is the
engine that underlies all evolutionary processes and that is responsible for
all but the simplest purposeful complexity.
The superscripts t and t+1 indicate the cycle number, and mt+1,
the number of inputs for a particular cycle, must always be less than or equal
to nt, the number of outputs in the previous cycle.
The most complex things in our world are also purposeful in the sense that they either fit logically with their environment or were intentionally made to fulfill some goal. Purposefully complex objects are always require extra information beyond that inherent in the laws of chemistry and physics. This extra information is usually in the form of instructions. Instructions are used to create configurations of matter that are far too improbable to occur without specification. Fundamentally, IPCS computations extract information from random choices accumulating information pertinent to whatever selection criteria are in play. All but the simplest of instructions owe their existence to some physical manifestation of the IPCS computation. Because of this, all truly complex objects can be understood in terms of evolution as computation.
Preprints of Research Papers on Complexity and Evolution:
Evolution as Computation, accepted for publication, July, 2004, in Evolutionary Theory {PDF}
Minimal History, a Theory of Plausible Explanation, (2007) Complexity 12, in press. {HTML}
Acquisition of General Adaptive Features by Evolution, Evolutionary Programming VII, VW. (1998) Porto et al eds, pp. 75-84. {PDF}
Department of Genetics, Development and Cell Biology
Interdepartmental Bioinformatics and Computational Biology Major
Interdepartmental Genetics Graduate Major
Interdepartmental Complex Adaptive Systems Graduate Minor
Laboratory for Nanoscale Self Assembly