DNA in nonliving systems
- From: "whitesickle@xxxxxxx" <whitesickle@xxxxxxx>
- Date: Thu, 2 Nov 2006 17:02:27 -0500 (EST)
Is my tooth brush made out of DNA? Is my printer made out of DNA? Is my
microwave made out of DNA? My "impression" was that DNA was the unit of
heredity which makes up "living things". So what am I to make when I
read, "All evolutionary systems employ a language in the specification
of the design of either a living or nonliving system. This
specification is called DNA, in parallel to the biological analog."
This suggests the evolutionary system of nonliving systems employs the
language DNA. It goes on to say, "In natural biological systems, the
representation is naturally compartmentalized." The researchers,
however, are currently investigating the limitations of
non-compartmentalized models of evolution. Soon, we will investigate
possible extensions of non-compartmentalized models to
compartmentalized models of evolution."
Is this somekind of theoretical mathematical game. How can DNA be the
design of a nonliving system? I've heard of DNA computers. What is the
definition of life here?
Michael Ragland
Compartmentalization in Evolution
Research Mentor :
Dr. Sanza T. Kazadi
JRI
JRI Research Mentor
sanza@xxxxxxxxx
Humankind currently enjoys the benefits of a great deal of technology.
This technology has been built up by our capability to understand how
things work and to manipulate parts of our real world. Historically
technology of two general types has been used in the generation of new
and exciting improvements in human capabilities. The first type
consists of those objects which have been used for a purpose other than
that originally intended. These include leaves used as roofing
material, cotton used as clothing, ropes fabricated from leaves, etc.
Although primitive in many respects, this technology is still in use
today, and its use will likely continue for some time. The second type
of technology is made up of devices, substances, and processes which
have been fabricated, using a complete understanding of the underlying
scientific processes. From petroleum, people have created a number of
synthetic substances, including very strong plastics, and synthetic
fabrics. From metals, people have created very strong and very light
substances, with metallurgy improving as our understanding of the
structure and chemistry of metals improved. Most recently,
microfabrication techniques and understanding of quantum effects has
effected our ability to create very small machines and devices,
generating our recent revolution in information technology. Recently,
the complexity of systems requiring human understanding has become
daunting, even for the most adept scientist or engineer. This has
necessitated the creation of computer aided design programs, allowing
the typical engineer to create and test designs with greater ease and
accuracy than ever before. However, despite the improvements, the
designs still require the engineer to be able to understand the device
as a combination of separated devices, rather than a complete whole.
Understanding the device not as a collection of separate devices but
rather as a whole device requires the development of techniques that
allow one to understand complex devices. Moreover, optimization of such
devices requires the simultaneous alteration of many devices at once,
which cannot be done by the typical engineer in a small amount of time.
In order to deal with these problems, a new set of algorithms has been
developed and is in development. These algorithms are known as
evolutionary algorithms . Evolutionary algorithms are designed to make
small modifications to a given design and to rank the improvement
against the previous design. Typically, many iterations of such small
improvements and rankings, strung together, will produce a final
optimal design.
All evolutionary systems employ a language in the specification of the
design of either a living or nonliving system. This specification is
called DNA, in parallel to the biological analog.Typically, it is
represented as a list of commands designed to provide information on
how to build the system. These commands are represented as a string of
commands, and interpreted by the system building the device. As it
turns out, the representation of the DNA is a critical issue in the
generation of quick evolutionary strategies. Improper representations
can lead to epistasis or stagnation in the evolutionary systems. In
natural biological systems, the representation is naturally
compartmentalized. This allows bits of DNA encoding specific tasks to
be duplicated and modified without damaging the functionality of the
other DNA, and allowing exploration of design space without destruction
of the design or organism. This is accomplished in part by using
transposons and genes. That these structures are ubiquitous is of
interest because it indicates that there might be a fundamental reason
for the success of such systems. Our research indicates that there is a
fundamental reason for the dominance of transposon-based systems. This
would seem to be a mathematical property of evolutionary systems, and
point to an important design constraint for evolutionary design
systems. We are currently investigating the limitations of
non-compartmentalized models of evolution. Soon, we will investigate
possible extensions of non-compartmentalized models to
compartmentalized models of evolution.
Publications
S. Kazadi, M. Lee, L. Lee A Case for Exhaustive Optimization
Proceedings of Gecco 2005 Conference, Late Breaking Papers , Washington
D.C., USA, June 2005. (Postscript) (PDF)
Abstract
Evolutionary algorithms have enjoyed a great success in a variety of
different fields ranging from numerical optimization to general
creative design. However, to date, the question of why this success is
possible has never been adequately determined. In this paper, we
examine two algorithms, a genetic algorithm and a pseudo-exhaustive
search algorithm dubbed Directed Exhaustive Search. We examine the GA's
apparent ability to compound individual mutations, and its role in the
GA's optimization. We then explore the use of the DES algorithm using a
suitably altered mutation operator mimicking the GA's surreptitious
compounding of the mutation operator. We find that the DES algorithm is
capable of performing comparably to or outperforming the GA over all
test problems, as predicted by theory.
S. Kazadi, D. Johnson, J. Melendez, B, Goo. Exhaustive Directed Search.
Proceedings of the Genetic and Evolutionary Computation Conference,
2004 , Seattle, WA, USA, 2004. (postscript) (PDF)
Abstract
We explore the development of an exhaustive directed search of state
space based on concepts from evolutionary computation. A brief
investigation of the evolvability of an evolutionary algorithm
illustrates that evolutionary algorithms are capable of reaching
optimal solutions when the diversification operator (which may be a
pseudo-operator which acts over many different diversification steps)
is capable of reaching, at every improvement point, another, more
improved population element. Moreover, we demonstrate that the upper
limit on the time to the optimal point is identical to that of an
exhaustive directed search. This search is exhaustive, but borrows the
diversification operator from the evolutionary algorithm and proceeds
in such a way that, if left alone, it would exhaustively search the
space. However, we demonstrate that this type of search can perform
comparably with the evolutionary algorithm, avoiding deceptive search
tracks that might trap an evolutionary algorithm.
S. Kazadi, D. Choi, A. Chang, T. Kang, H. Li, D. Kim, S. Ho, J. Wu. On
the Design of an Evolutionary Preprocessor. Proceedings of the Genetic
and Evolutionary Computation Conference, 2003 , Chicago, IL, USA, 2003.
(postscript) (PDF)
Abstract
In this paper we explore methods of enhancing the evolvability of a
particular device. We assume that the device may be specified by a
table of inputs and outputs. We investigate a method of extracting the
topologial structure of the device from rarified absolute Hessian
matrices (raH matrices) and using this topological information as the
basis for construction of solutions to evolutionary problems. We
validate the algorithm by demonstrating its ability to extract the
structure of devices to be evolved from the input/output table.
Moreover, we validate this structure by using a genetic algorithm to
train a perceptron, yielding perceptrons which solve the computational
problem with error rates of less than 4\%.
S. Kazadi, S. Cheung, C. Ogletree, S. Kim, C. Lee, A. Min. A Study of
Evolutionary Acceleration. Proceedings of the Genetic and Evolutionary
Computation Conference, 2003 , Chicago, IL, USA, 2003. (postscript)
(PDF)
Abstract
We investigate the phenomenon of numerical evolutionary acceleration.
This phenomenon is a simple consequence of numerical analysis of the
probabilities of evolving independent parts of a complex system in the
presence of evolutionary epochs. The epoch mechanism allows the newly
evolved structure to become part of the overall system design of all
elements of the population. We demonstrated that this phenomenon not
only exists in real evolving systems, but that evolutionary
acceleration dwarfs the group mechanism for some complex structures.
S. Kazadi, Y. Qi, I. Park, N. Huang, P. Hwu, B. Kw an, W. Lue, and H.
Li. Insufficiency of Piecewise Evolution. Proceedings of the Third
NASA/DoD Workshop on Evolvable Hardware , Long Beach, CA, 2001.
(postscript) (PDF)
Abstract
We describe an evolutionary design paradigm called piecewise evolution
... This evolutionary design paradigm allows the gradual evolution of a
piece of hardware using discrete functional stages. The paradigm
removes designs from a population of designs which effectively lose
functionality already discovered. Significant improvements in the
evolution time of simple one-bit adders are reported. However,
evolution of more complex devices does not seem to share the
improvements in evolutionary speed of simple devices. These results are
discussed in the context of epistasis and deceptiveness.
S. Kazadi, D. Lee, R. Modi, J. Sy, W. Lue. Levels of
Compartmentalization in Artificial Evolution. Proceedings of GECCO 2000
, pp.841-849, 2000. (postscript) (PDF)
Abstract
This paper addresses the use of particular encoding schemes in
evolutionary systems. We define three paradigms of DNA encodings:
non-compartmentalized DNA , partially compartmentalized DNA, and fully
compartmentalized DNA. We demonstrate that there is a significant and
increasing advantage to the use of partially and fully
compartmentalized models as the complexity of a structure increases.
Implications for the design of evolutionary systems including
biological systems are discussed.
S. Kazadi, D. Lee, R. Modi, J. Sy, W. Lue. Levels of
Compartmentalization in Artificial Life. Proceedings of Artificial Life
VII , Bedau, McCaskill, Packard, and Rasmussen, eds.: MIT Press, 81-89,
2000. (postscript) (PDF)
.
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