Design Optimization of Artificial Evolutionary Systems
Created by W.Langdon from
gp-bibliography.bib Revision:1.7975
- @PhdThesis{Suzuki:thesis,
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author = "Hideaki Suzuki",
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title = "Design Optimization of Artificial Evolutionary
Systems",
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school = "Graduate School of Informatics, Kyoto University",
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year = "2004",
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type = "Doctor of Informatics",
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address = "Japan",
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month = oct,
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keywords = "genetic algorithms, genetic programming, alife,
chemical genetic programming",
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URL = "http://www.nis.atr.jp/~hsuzuki/papers/2004_Dissertation.pdf",
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size = "161 pages",
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abstract = "The performance of an artificial evolutionary system
is largely determined by the basic design prepared by a
human designer. This thesis describes a sequence of the
author's studies that aim at improving the design and
implementing a computational system able to evolve
complex programs or solutions in a life-like way. The
thesis first describes background theories on the
design in artificial life (alife). From the comparison
to the biological system, several design criteria on
alife systems are presented and representative alife
systems are assessed under the criteria. A mathematical
theory for the analysis on a creature genotype space is
also described. Next, the thesis proposes a machine
language core memory system, SeMar. SeMar is designed
using a strong comparison between computation and
biochemical reactions. In imitation of biological
molecules, four kinds of data words are prepared in the
core. They are Membrane, Nutrient, DNA, and Protein,
and in the revised form of SeMar, all of the core
reactions are propelled by the parallel Protein
execution. The possibility of evolution of complex
programs in SeMar is discussed based on experimental
results and design criteria for an alife system. Then,
the thesis considers evolvability of artificial
evolutionary systems in general. Considering the
relation between evolvability and the fitness
landscape, a measure that parametrizes evolvability of
alife systems is proposed, and the design of an example
alife system, a string rewriting system, is numerically
optimized in terms of the maximization of the measure.
Experimental results show that numerical optimization
by a computer can find a far better design than that
prepared by a human. In addition, using the same
system, the connectivity of viable genotypes in the
genotype space (evolvability) is examined as a function
of the measure, demonstrating strong correlation
between evolvability and the measure. In the final
part, the thesis proposes a new evolutionary
optimization algorithm named chemical genetic
algorithms (CGAs). The CGA focuses on an important
factor of the artificial evolutionary system design,
translation. Mimicking the biological translation in a
living cell, the CGA uses cellular structure with DNA
and other smaller molecules for translation as a
selection unit, enabling coevolution of DNA information
and translation. Numerical experiments reveal that the
CGA can optimize the translation, smooth the fitness
landscape and enhance the GA's evolvability, and as a
consequence, have high performance with a wide range of
functional optimization problems.",
- }
Genetic Programming entries for
Hideaki Suzuki
Citations