I have to say that generative encodings are the most exciting new development of genetic algorithms.
Basically, the idea is this: Typically in GAs, the genotype (the bits that make up the genome, the ones that undergo selection and recombination) maps directly to a phenotype (one to one).
So if you were going to evolve a brain, there would have to be one gene for each connection in the brain. The problem is, such a direct 1-1 encoding can't scale to huge brains. For example in the human brain there are trillions of connections (synapses), which would mean searching a trillion-dimensional space (unfathomably difficult). Nature's solution is to use development, where a brain grows over time, where the genes don't directly specify each connection, but the blueprint for how a brain should grow.
So, in generative encodings in GAs, the principle is simlar. An indirect (or generative) encoding is where one gene can represent an aspect of a developmental process instead of directly specifying each component. In some ways it's similar to how in engineering you reuse components when making a building; if the manufacture of each nail had to be specified completely separately a project would be overwhelming.
So, in generative encodings in GAs, the principle is simlar. An indirect (or generative) encoding is where one gene can represent an aspect of a developmental process instead of directly specifying each component.
I haven't heard of generative encoding before -- it sounds really cool. I could imagine something similar done with Genetic Programming. You could design a DSL (___domain specific language) that is capable of building Neural Nets and then perform GP on programs written in the DSL. This could take the pressure off having to design a good development algorithm -- the GP would would do that for you.
Basically, the idea is this: Typically in GAs, the genotype (the bits that make up the genome, the ones that undergo selection and recombination) maps directly to a phenotype (one to one).
So if you were going to evolve a brain, there would have to be one gene for each connection in the brain. The problem is, such a direct 1-1 encoding can't scale to huge brains. For example in the human brain there are trillions of connections (synapses), which would mean searching a trillion-dimensional space (unfathomably difficult). Nature's solution is to use development, where a brain grows over time, where the genes don't directly specify each connection, but the blueprint for how a brain should grow.
So, in generative encodings in GAs, the principle is simlar. An indirect (or generative) encoding is where one gene can represent an aspect of a developmental process instead of directly specifying each component. In some ways it's similar to how in engineering you reuse components when making a building; if the manufacture of each nail had to be specified completely separately a project would be overwhelming.