A Taxonomy for Artificial Embryogeny

@article{Stanley2003ATF,
  title={A Taxonomy for Artificial Embryogeny},
  author={Kenneth O. Stanley and Risto Miikkulainen},
  journal={Artificial Life},
  year={2003},
  volume={9},
  pages={93-130}
}
A major challenge for evolutionary computation is to evolve phenotypes such as neural networks, sensory systems, or motor controllers at the same level of complexity as found in biological organisms. In order to meet this challenge, many researchers are proposing indirect encodings, that is, evolutionary mechanisms where the same genes are used multiple times in the process of building a phenotype. Such gene reuse allows compact representations of very complex phenotypes. Development is a… 
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503 Citations

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References

SHOWING 1-10 OF 140 REFERENCES

A Developmental Model for the Evolution of Artificial Neural Networks

This work presents a model of decentralized growth and development for artificial neural networks (ANNs), inspired by developmental biology and the physiology of nervous systems, and demonstrates the power of the artificial chemistry by analyzing engineered genomes that lead to the growth of simple networks with behaviors known from physiology.

Cell division and migration in a ‘genotype’ for neural networks

This paper presents a model of neural development which includes cell division and cell migration in addition to axonal growth and branching, and finds that mutations that affect different phases of development have very different evolutionary consequences.

Repeated structure and dissociation of genotypic and phenotypic complexity in artificial ontogeny

It is demonstrated that evolved genetic regulatory networks in AO give rise to hierarchical, repeated phenotypic structures, and the claim that artificial ontogeny is a useful design tool for the evolutionary design of virtual agents and real-world robots is supported.

Toward an evolvable model of development for autonomous agent synthesis

A simplified yet biologically defensible model of the developmental process of autonomous agents, specifically one that has agent-like properties is presented and it is demonstrated that this developmental model can be evolved.

A Gene Network Model for Developing Cell Lineages

A dynamic recurrent gene network (DRGN) model was designed and its ability to control the developmental trajectories of cells during embryogenesis was evaluated, demonstrating the ability of DRGNs to perform the tasks with minimal external input.

From Evolutionary Computation to Computational Evolution

It is proposed that evolutionary computation be understood more like computational evolution, i.e., to use the evolutionary process for construction, rather than for optimization, and to construct intelligence as adaptive behavior based on artiicial neural networks.

A connectionist model of development.

The Role of Development in Genetic Algorithms

Growing neural networks

Simulation results shed some light on why modular architectures are likely to emerge, why similar successions of stages tend to appear in both evolution and development, and why a developmental age is preserved evolutionarily although the mature state may appear to be more efficient from the point of view of fitness.

Three Ways to Grow Designs: A Comparison of Embryogenies for an Evolutionary Design Problem

The results are surprising, with the implicit embryogeny outperforming all other techniques by showing no significant increase in the size of the genotypes or decrease in accuracy of evolution as the scale of the problem is increased.
...