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130 Biomimetics: Biologically Inspired Technologies

functionality? This is the problem of Synthesis. Although engineers practice it and teach it all the
time, we do not have a formal model of how open-ended synthesis can be done automatically.
Applications are numerous. This is the meta-problem of engineering: Design a machine that can
design other machines.
The example above is confined to electromechanics, but similar synthesis challenges occur in
almost all engineering disciplines: circuits, software, structures, robotics, control, and MEMS to
name a few. Are there fundamental properties of design synthesis that cut across engineering fields?
Can a computer ultimately augment or replace human invention?
While we may not know how to synthesize things automatically, nature may give us some clues;
after all, the fascinating products of nature were designed and fabricated autonomously.
In the last two centuries, engineering sciences have made remarkable progress in the ability
to analyze and predict physical phenomena. We understand the governing equations of thermo-
dynamics, elastics, fluid flow, and electromagnetics to name a few domains. Numerical
methods such as finite elements allow us to solve these differential equations, with good approxi-
mation, for many practical situations. We can use these methods to investigate and explain
observations, as well as to predict the behavior of products and systems long before they are
physically realized.
But progress in systematic synthesis has been much slower. For example, the systematic
synthesis of a kinematic machine for a given purpose is a long-standing problem, and perhaps
one of the earliest general synthesis problems to be posed. Robert Willis, a professor of natural and
experimental philosophy at Cambridge, wrote in 1841:

[A rational approach to synthesis is needed] to obtain, by direct and certain methods, all the forms
and arrangements that are applicable to the desired purpose. At present, questions of this kind
can only be solved by that species of intuition that which long familiarity with the subject usually
confers upon experienced persons, but which they are totally unable to communicate to others. When
the mind of a mechanician is occupied with the contrivance of a machine, he must wait until, in the
midst of his meditations, some happy combination presents itself to his mind which may answer
his purpose.
Robert Willis, Principles of Mechanism (Willis, 1841)
Almost two centuries later, a rational method for synthesis in many domains is still not clear.
Though many best-practice design methodologies exist, at the end of the day they rely on elusive
human creativity. Product design is still taught today largely through apprenticeship: engineering
students learn about existing solutions and techniques for well-defined, relatively simple problems,
and then — through practice — are expected to improve and combine these to create larger, more
complex systems. How is this synthesis process achieved? We do not know, but we cloak it with the
term ‘‘creativity.’’
The question of how synthesis of complex systems occurs has been divided in a dichotomy
of two views. One view is that complex systems emerge through successive adaptations coupled
with natural selection. This Darwinian process is well accepted in biology, but is more controversial
in engineering (Basalla, 1989; Ziman, 2003). The alternative explanation is intelligent design,
mostly rejected in biology, but still dominant in engineering — as the celebrated revolutionary
inventor.
The process of successive adaptation by improvement and recombination of basic building
blocks is evolutionary in its nature. Unlike classical genetic algorithms (e.g., Goldberg, 1989,
Chapter 5), however, it is open-ended: We do not know a ´ priori what components we will need and
how many of them. The permutation space is exponential, and complexity is unbounded. This is
perhaps a subtle but key difference between optimization (e.g., Papadimitriou and Steiglitz, 1998)
and synthesis. In optimization problems, we tune the values of a set of parameters in order to