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  • Articles  (2)
  • Immunocytochemistry
  • evolution
  • Springer  (2)
  • Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics  (2)
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  • Articles  (2)
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  • Springer  (2)
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  • 1
    Electronic Resource
    Electronic Resource
    Micromechanical identification of anisotropic damage evolution laws (1999)
    Springer
    International journal of fracture 98 (1999), S. 55-76 
    Details
    ISSN: 1573-2673
    Keywords: Anisotropic ; damage ; evolution ; crack tenso.
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: Abstract This paper deals with the establishment of anisotropic conjugate force based damage evolution laws in the framework of Rice's (1971) ‘normality structure’. The damage variable is the second-order crack tensor (Kachanov, 1980), which represents preexisting Griffith microcracks in a solid. The principal results include the deduced damage surfaces, potentials and kinetic equations for the basic internal variables and damage tensor during isothermal processes. The generalized pth order crack tensors and qth order energy release rates are introduced. The deduction in this paper is fully independent of the specific form of the free energy or Gibbs energy functions, so the deduced damage evolution laws have a wide applicable range including plasticity. Using the deviatoric stress as the conjugate force, the two well-established anisotropic yield surfaces, Karafillis and Boyce (1993) and Hill (1950), are recovered from the deduced damage surface.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1018787705489
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  • 2
    Electronic Resource
    Electronic Resource
    Learning and Evolution (1999)
    Springer
    Autonomous robots 7 (1999), S. 89-113 
    Details
    ISSN: 1573-7527
    Keywords: learning ; evolution ; plastic individuals ; Baldwin Effect
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: Abstract In the last few years several researchers have resorted to artificial evolution (e.g., genetic algorithms) and learning techniques (e.g., neural networks) for studying the interaction between learning and evolution. These studies have been conducted for two different purposes: (a) looking at the performance advantages obtained by combining these two adaptive techniques; (b) understanding the role of the interaction between learning and evolution in biological organisms. In this paper we describe some of the most representative experiments conducted in this area and point out their implications for both perspectives outlined above. Understanding the interaction between learning and evolution is probably one of the best examples in which computational studies have shed light on problems that are difficult to study with the research tools employed by evolutionary biology and biology in general. From an engineering point of view, the most relevant results are those showing that adaptation in dynamic environments gains a significant advantage by the combination of evolution and learning. These studies also show that the interaction between learning and evolution deeply alters the evolutionary and the learning process themselves, offering new perspectives from a biological point of view. The study of learning within an evolutionary perspective is still in its infancy and in the forthcoming years it will produce an enormous impact on our understanding of how learning and evolution operate.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1008973931182
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    Paper (German National Licenses)
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