Evolving neural networks through augmenting topologies
Evolutionary Computation
Adding Continuous Components to L-Systems
L Systems, Most of the papers were presented at a conference in Aarhus, Denmark
A Taxonomy for artificial embryogeny
Artificial Life
Compositional pattern producing networks: A novel abstraction of development
Genetic Programming and Evolvable Machines
A novel generative encoding for exploiting neural network sensor and output geometry
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Generating large-scale neural networks through discovering geometric regularities
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Acquiring evolvability through adaptive representations
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Generative encoding for multiagent learning
Proceedings of the 10th annual conference on Genetic and evolutionary computation
The sensitivity of HyperNEAT to different geometric representations of a problem
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
A case study on the critical role of geometric regularity in machine learning
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Competitive coevolution through evolutionary complexification
Journal of Artificial Intelligence Research
Augmenting artificial development with local fitness
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Evolving complete robots with CPPN-NEAT: the utility of recurrent connections
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Picbreeder: A case study in collaborative evolutionary exploration of design space
Evolutionary Computation
On the Performance of Indirect Encoding Across the Continuum of Regularity
IEEE Transactions on Evolutionary Computation
Ribosomal robots: evolved designs inspired by protein folding
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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In evolutionary computation it is common for the genotype to map directly to the phenotype such that each gene in the genotype represents a single discrete component of the phenotype. While this convention is widespread, in nature the mapping is not direct. Instead, the genotype maps to the phenotype through a process of development, which means that each gene may be activated multiple times and in multiple places in the process of constructing the phenotype. This tutorial will examine why such indirect mapping may be critical to the continuing success of evolutionary computation. Rather than just an artifact of nature, indirect mapping means that vast phenotypic spaces (e.g. the 100 trillion connection human brain) can be searched effectively in a space of far fewer genes (e.g. the 30,000 gene human genome). The tutorial will introduce this research area, called Generative and Developmental Systems (GDS), by surveying milestones in the field, exploring GDS-based representations, and introducing its most profound puzzles. Most importantly, what is the right abstraction of natural development to capture its essential advantages without introducing unnecessary overhead into the search?