1993*Analogy-Making as Perception*.1996*An Introduction to Genetic Algorithms*.1998, with Tino Gramss, Stefan Bornholdt, Michael Gross, Thomas Pellizzari*Non-Standard Computation*.*Perspectives on Adaptation in Natural and Artificial Systems*. 2005, with Lashon B. Booker, Stephanie Forrest, Rick L. Riolo2009*Complexity*.

- The Emergence of Understanding in a Computer Model of Concepts and Analogy-Making. 1990. (In
*Emergent Computation*) - The Royal Road for Genetic Algorithms: Fitness Landscapes and GA Performance. 1992. (In
*Toward a Practice of Autonomous Systems*) - Dynamics, Computation, and the "Edge of Chaos": a re-examination. 1994. (In
)*Complexity: metaphors, models, and reality* - Genetic Algorithms and Artificial Life. 1995. (In
)*Artificial Life: an Overview* - Computation in Cellular Automata: A Selected Review. 1998. (In
)*Non-Standard Computation* - Analogy making as a complex adaptive system. 2001. (In
*Design Principles for the Immune System and Other Distributed Autonomous Systems*) - Evolutionary design of collective computation in cellular automata. 2003. (In
*Evolutionary Dynamics*)

An in-depth description of *Copycat*, one of the projects at
Douglas Hofstadter's
Fluid Analogies Research Group

- Melanie Mitchell's own
`summary`

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
the study of genetic algorithms has shown 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

- • Kenneth De Jong.
**Genetic Algorithms: A 30 Year Perspective**. 2005 - • John R. Koza.
**Human-Competitive Machine Intelligence by Means of Genetic Algorithms**. 2005 - • David E. Goldberg.
**John Holland, Facetwise models, and Economy of Thought**. 2005 - • Arthur W. Burks.
**An Early Graduate Program in Computers and Communications**. 2005 - • Oliver G. Selfridge.
**Had We But World Enough and Time**. 2005 - • Bernard P. Zeigler.
**Discrete Event Abstraction: An Emerging Paradigm for Modeling Complex Adaptive Systems**. 2005 - • Herbert A. Simon.
**Good Old-Fashioned AI and Genetic Algorithms: An Exercise in Translation Scholarship**. 2005 - • Douglas R. Hofstadter.
**Moore's Law, Artificial Evolution, and the Fate of Humanity**. 2005 - • Julian Adams.
**Evolution of Complexity in Microbial Populations**. 2005 - • Bobbi S. Low, Doug Finkbeiner, Carl Simon.
**Favored Places in the Selfish Herd: Trading Off Food and Security**. 2005 - • Rick L. Riolo, Robert Axelrod, Michael D. Cohen.
**Tags, Interaction Patterns and the Evolution of Cooperation**. 2005 - • Robert G. Reynolds, Salah Saleem.
**The Impact of Environmental Dynamics on Cultural Emergence**. 2005 - • Kenneth J. Arrow.
**John Holland and the Evolution of Economics**. 2005 - • W. Brian Arthur.
**Cognition: The Black Box of Economics**. 2005