Computational Evolutionary Embryogeny

• Main Author: Yogev, Or
• Format: Others
• Published: 2009
• Online Access: https://thesis.library.caltech.edu/208/1/yogev2008.pdf; Yogev, Or (2009) Computational Evolutionary Embryogeny. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/N4XG-F402. https://resolver.caltech.edu/CaltechETD:etd-01162009-072031 <https://resolver.caltech.edu/CaltechETD:etd-01162009-072031>

<p>Evolution and development (Evo-Devo), are the two main processes which produce all of the different kinds of phenotypes we see in nature. Evolutionary process is responsible for eliminating the genetic information of weak phenotypes through natural selection, and also for exploring novel genotypes through genetic operations; crossover, mutation. The development process is the process of using the set of rules (codons) written in a genome, to turn a single set (zygote) into a mature phenotype. In this thesis, evolutionary and developmental processes are used to evolve the configurations of three-dimensional structures in silico to achieve desired performances. Although natural systems utilize the combination of both evolution and development processes to produce remarkable performance and diversity, this approach has not yet been applied extensively to the design of continuous three-dimensional load-supporting 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.</p> <p>Modularity and symmetry are visible in nearly every natural and engineered structure. 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.</p> <p>The computational evolutionary development approach presented here shows promise for synthesizing novel configurations of high-performance systems. The approach may advance system design to a new paradigm, where current design strategies have difficulty producing useful solutions. In addition to a new design approach perse, this model gives us the ability to explore the development process, from the standpoint of complex systems analysis. The phenotypes in our system have been grown under a highly stochastic environment, which serves as a triggered mechanism for gene expression. Still, evolution was able to find solutions which are robust to these stochastic elements, both at the phenotype level (the phenotype ability to function under the environment) and the growth process itself. In addition we have also explored the effects of symmetric and nonsymmetric environment over the topology of the phenotypes; we have found strong evidence that indicates a high correlation between the two. Finally we have also established a tool which enables us to understand the relationship between the environment and the degree of modularity of the phenotype.</p>