Design Optimization of Artificial Evolutionary Systems

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  author =       "Hideaki Suzuki",
  title =        "Design Optimization of Artificial Evolutionary
  school =       "Graduate School of Informatics, Kyoto University",
  year =         "2004",
  type =         "Doctor of Informatics",
  address =      "Japan",
  month =        oct,
  keywords =     "genetic algorithms, genetic programming, alife,
                 chemical genetic programming",
  URL =          "",
  size =         "161 pages",
  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