One of the main limitations of scalability in body-brain evolution systems is the representation chosen for encoding creatures. This paper defines a class of representations called generative representations, which are identified by their ability to reuse elements of the genotype in the translation to the phenotype. This paper presents an example of a generative representation for the concurren...

We focus on generative autoencoders, such as variational or adversarial autoencoders, which jointly learn a generative model alongside an inference model. Generative autoencoders are those which are trained to softly enforce a prior on the latent distribution learned by the inference model. We call the distribution to which the inference model maps observed samples, the learned latent distribut...

One of the applications of evolutionary algorithms is the automatic creation of designs. For evolutionary techniques to scale to the complexities necessary for actual engineering problems, it has been argued that generative systems, where the genotype is an algorithm for constructing the final design, should be used as the encoding. We describe a system for creating generative specifications by...

Deep generative models and their associated top-down architecture are gaining popularity in neuroscience and computer vision. In this paper we link our previous work with regulatory feedback networks to generative models. We show that generative model’s and regulatory feedback model’s equations can share the same fixed points. Thus, phenomena observed using regulatory feedback can also apply to...

Generative kernels represent theoretically grounded tools able to increase the capabilities of generative classification through a discriminative setting. Fisher Kernel is the first and mostly-used representative, which lies on a widely investigated mathematical background. The manufacture of a generative kernel flows down through a two-step serial pipeline. In the first, “generative” step, a g...

Many approaches to learning classifiers for structured objects (e.g., shapes) use generative models in a Bayesian framework. However, state-of-the-art classifiers for vectorial data (e.g., support vector machines) are learned discriminatively. A generative embedding is a mapping from the object space into a fixed dimensional feature space, induced by a generative model which is usually learned ...

The present paper deals with the description of computer testing of the functional generative description (proposed by P. Sgall). Illustrations are given of the system of programmes for testing the generative component of the grammar, and some aspects of notation necessitated by the specific requirements of computer testing are pointed out. 1. The problem whether a particular generative grammar...