1998, with Tino Gramss, Michael Gross, Melanie Mitchell, Thomas Pellizzari*Non-Standard Computation*.*Handbook of Graphs and Networks*. 2003, with Heinz Georg Schuster

- Genetic Algorithms. 1998. (In
)*Non-Standard Computation* - The dynamics of large biological systems: a statistical physics view of macroevolution. 2003. (In
*Evolutionary Dynamics*)

There’s never enough computer power for challenging questions.
Problems such as the design of turbines consisting of more than 100 parts
or the simulation of systems of some 50 interacting particles
are far beyond today’s computer capacities.
Or, how to find the shortest phone line connecting 100 given cities?

The most promising answers to such questions come from unconventional technologies.
The massive parallelism of **molecular computers** or
the ingenious use of quantum systems by **universal quantum computers**
provide solutions to the dilemma.
Designed to solve specific problems, they show unprecedented performance.

And as for the phone line problem – **genetic algorithms** mimick
the way nature found its way from the first cells to today’s creatures.
While relying on conventional computer hardware,
they introduce an element of chance on the software level,
thus circumventing the disadvantages of traditional deterministic algorithms.

A textbook for those shaping the future of computing, this volume is also pure fun. Learn about the physical principles of tomorrow’s technology, and enjoy a marvellous tour through their potential applications!

This promised to be a very interesting book, but it was let down for me
by being too low level – too much about the scientific and technological
bases, and not enough about any new *computational paradigms*. (The
very poor level of proof reading, with some chapters thick with spelling
mistakes, also detracts.)

I was hoping for an overview of what new tools are being added to our computational capability, with maybe a review of the current state of the art, but what I got was a bunch of essays that have an idiosyncratic viewpoint, with all the details in the wrong places (for me, at least).

For example, the chapter on Genetic Algorithms devotes hardly any space
to the schemata model (beyond saying it is intuitive) but instead develops
a “statistical mechanics” model, without then providing the
intuition of how this model helps us to cast or solve new computational
problems. It also seems to imply that *mutation* is the key concept,
with *cross-over* just an interesting second order add-on (whereas
studies of some genetic algorithms show is that cross-over is key, with
mutation playing a surprisingly small role).

The two chapters on quantum computing range over the theoretical QM
underpinnings, and the current technology, but again provide no intuition
of how these devices work *as computers*. (And the second of these
chapters has an almost useless bibliography, since it omits the papers’
titles.)

So I was left disappointed.

- • Heinz Georg Schuster.
**Introduction to Non-standard Computation**. 1998 - • Michael Gross.
**Molecular Computation**. 1998 - Using DNA to solve computational problems can allow massive
parallelism, by exploring 10
^{19}cases in parallel. This doesn't solve the trouble with exponentially difficult problems -- but it does delay it by several orders of magnitude! Currently, to program such a device, one needs to be a good bench chemist -- by DNA-sequencing technologies might make automatic compilation easier.

Supramolecules are large molecules that are (partly) built from*non*-covalent bonds. Natural supramolecules include DNA; artificial ones also look promising for computational applications. - • Stefan Bornholdt.
**Genetic Algorithms**. 1998 - Solving optimisation problems with artificial evolution
- • Melanie Mitchell.
**Computation in Cellular Automata: A Selected Review**. 1998 - A good overview of what cellular automata are, and a clear
description of how higher-level 'particles' can be used to design
cellular automata algorithms. [By far the best chapter. I find cellular
automata intrinsically interesting, but this chapter still left me
wondering what advantages they have
*as computers*.] - • Tino Gramss.
**The Theory of Quantum Computation: An Introduction**. 1998 - Theoretical quantum mechanic underpinnings of quantum computers -- all Hamiltonians and reversibility
- • Thomas Pellizzari.
**Quantum Computers: First Steps Towards a Realization**. 1998 - Current technology for building (very small!) quantum computers, including error correction

- • Bela Bollobas, Oliver M. Riordan.
**Mathematical results on scale-free random graphs**. 2003 - • Mark E. J. Newman.
**Random graphs as models of networks**. 2003 - • Albert-László Barabási.
**Emergence of scaling in complex networks**. 2003 - • Reuven Cohen, Shlomo Havlin, Daniel ben-Avraham.
**Structural properties of scale-free networks**. 2003 - • Romualdo Pastor-Satorras, Alessandro Vespignani.
**Epidemics and immunization in scale-free networks**. 2003 - • Ralf J. Sommer.
**Cells and genes as networks in nematode development and evolution**. 2003 - • Ricard V. Sole, Romualdo Pastor-Satorras.
**Complex networks in genomics and proteomics**. 2003 - • Sergei Maslov, Kim Sneppen, Uri Alon.
**Correlation profiles and motifs in complex networks**. 2003 - • Wolfgang Kinzel.
**Theory of interacting neural networks**. 2003 - • Barbara Drossel, Alan J. McKane.
**Modelling food webs**. 2003 - • Kai Nagel.
**Traffic networks**. 2003 - • Alan Kirman.
**Economic networks**. 2003 - • Lada A. Adamic, Rajan M. Lukose, Bernardo A. Huberman.
**Local search in unstructured networks**. 2003 - • Sergey(Sergei) N. Dorogovtsev, Jose F. F. Mendes.
**Accelerated growth of networks**. 2003 - • Gerard Weisbuch, Sorin Solomon.
**Social percolators and self organized criticality**. 2003 - • Sanjay Jain, Sandeep Krishna.
**Graph theory and the evolution of autocatalytic networks**. 2003