Computational Evolutionary Embryogeny

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 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. 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

Engineering by fundamental elements of evolution

The method presented in this note mimics two fundamental mechanisms from nature, growth, and development, for the synthesis of new three-dimensional structures. The structures were synthesized to support a load generated by a wind. Every structure grows from a single artificial cell following a set of genes, encoded in an artificial genome shared by all cells. Genes are a set of commands that control the growth process. Genes are regulated by interaction with the environment. The environment is both external and internal to the structure. The performance each structure is measured by its ability to hold the load and other additional engineering criteria. A population of structures is evolved using a genetic algorithm, which alters the genome of two mating individuals. We will present evolved phenotypes with high degrees of modularity and symmetry which evolved according to engineering criteria. Neither one of these two characteristics has been directly imposed as the fitness evaluation, but rather spontaneously emerge as a consequence of natural selection. We will argue that the types of rules we are using in this model are not biased toward any of these characteristics, but rather basic rules for growth and development

An investigation into the structure of genomes within an evolution that uses embryogenesis

Evolutionary algorithms that use embryogenesis in the creation of individuals have several desirable qualities. Such algorithms are able to create complex, modular designs which can scale well to large problems. However, the inner workings of developmental algorithms have not been investigated as thoroughly as their direct-encoding counterparts. More precisely, it would be beneficial to look at how the rules used during embryogenesis evolve alongside the phenotypes they produced. This paper reports on such an investigation into the evolution of a rule set for the growth of an artificial neural network, and identifies several aspects that are desirable for the genomes of a developmental evolutionary algorithm

Interatomic scattering in energy dependent photoelectron spectra of Ar clusters

Soft X-ray photoelectron spectra of Ar 2p levels of atomic argon and argon clusters are recorded over an extended range of photon energies. The Ar 2p intensity ratios between atomic argon and clusters’ surface and bulk components reveal oscillations similar to photoelectron extended X-ray absorption fine structure signal (PEXAFS). We demonstrate here that this technique allows us to analyze separately the PEXAFS signals from surface and bulk sites of free-standing, neutral clusters, revealing a bond contraction at the surface

A novel evolutionary method for synthesis of 3D continuous structures

The design of complex structures which benefit the usage of inhomogeneous properties is a very difficult task. In this paper we present a novel approach in which we synthesize the design of structures by mimicking two fundamental processes from biology - Evolution and Development. We will show that by using these two processes in a computational model, we are able to evolve high performance structures. These structures contain a high degree of complexity from a topological aspect and from a materials distribution aspect. This degree of complexity is difficult or even impossible to achieve by ordinary design methods

Modularity and symmetry in computational embryogeny

Modularity and symmetry are two properties observed in almost every engineering and biological structure. The origin of these properties in nature is still unknown. Yet, as engineers we tend to generate designs which share these properties. In this paper we will report on the origin of these properties in three dimensional evolved structures (phenotypes). The phenotypes were evolved in an evolutionarydevelopmental model of biological structures. The phenotypes were grown under a high volatility stochastic environment. The phenotypes have evolved to function within the environment using the very basic requirements. Even though neither modularity nor symmetry have been directly imposed as part of the requirements, the phenotypes were able to generate these properties after only a few hundred generations. These results may suggest that modularity and symmetry are both very fundamental properties that develop during the early stages of evolution. This result may give insight to the origin of both modularity and symmetry in biological organisms

An investigation into the structure of genomes within an evolution that uses embryogenesis

Evolutionary algorithms that use embryogenesis in the creation of individuals have several desirable qualities. Such algorithms are able to create complex, modular designs which can scale well to large problems. However, the inner workings of developmental algorithms have not been investigated as thoroughly as their direct-encoding counterparts. More precisely, it would be beneficial to look at how the rules used during embryogenesis evolve alongside the phenotypes they produced. This paper reports on such an investigation into the evolution of a rule set for the growth of an artificial neural network, and identifies several aspects that are desirable for the genomes of a developmental evolutionary algorithm