Computational evolutionary embryogeny

  • Authors:

  • Or Yogev;Andrew A. Shapiro;Erik K. Antonsson

  • Affiliations:

  • eSolar, Inc., Pasadena, CA and Engineering Design Research Laboratory, Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA;Department of Electrical Engineering and Computer Science, University of California, Irvine, CA and Enterprise Engineering Division, Jet Propulsion Laboratory, California Institute of Technology, ...;Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA

  • Venue:
  • IEEE Transactions on Evolutionary Computation
  • Year:
  • 2010

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Abstract

Evolutionary and developmental processes are used to evolve the configurations of 3-D structures in silico to achieve desired performances. Natural systems utilize the combination of both evolution and development processes to produce remarkable performance and diversity. However, this approach has not yet been applied extensively to the design of continuous 3-D loadsupporting structures. Beginning with a single artificial cell containing information analogous to a DNA sequence, a structure is grown according to the rules encoded in the sequence. Each artificial cell in the structure contains the same sequence of growth and development rules, and each artificial cell is an element in a finite element mesh representing the structure of the mature individual. Rule sequences are evolved over many generations through selection and survival of individuals in a population. Modularity and symmetry are visible in nearly every natural and engineered structure. An understanding of the evolution and expression of symmetry and modularity is emerging from recent biological research. Initial evidence of these attributes is present in the phenotypes that are developed from the artificial evolution, although neither characteristic is imposed nor selected-for directly. The computational evolutionary development approach presented here shows promise for synthesizing novel configurations of high-performance systems. The approach may advance the system design to a new paradigm, where current design strategies have difficulty producing useful solutions.