Design Optimization of Artificial Evolutionary Systems

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@PhdThesis{Suzuki:thesis,

• author = "Hideaki Suzuki",
• title = "Design Optimization of Artificial Evolutionary Systems",
• 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 = "http://www.nis.atr.jp/~hsuzuki/papers/2004_Dissertation.pdf",
• 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

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