Automatic Definition of Modular Neural Networks

This paper illustrates an artificial developmental system that is a computationally efficient technique for the automatic generation of complex Artificial Neural Networks (ANN). Artificial developmental system can develop a graph grammar into a modular ANN made of a combination of more simple subnetworks. A genetic algorithm is used to evolve coded grammars that generates ANNs for controlling a six-legged robot locomotion. A mechanism for the automatic definition of sub-neural networks is incorporated. Using this mechanism, the genetic algorithm can automatically decompose a problem into subproblems, generate a subANN for solving the subproblem, and instantiate copies of this subANN to build a higher level ANN that solves the problem. We report some simulation results showing that the same problem cannot be solved if the mechanism for automatic definition of sub-networks is suppressed. We support our argumentation with pictures describing the steps of development, how ANN structures ar..

Distributed execution of automata networks on a computing medium: introducing IfAny machines.

International audienceA computing medium is a set of Processing Elements (PE) homogeneously distributed in space, with connections local in space. PEs are fine grain, and are therefore modeled as Finite State Machine (FSM). In this elementary framework, the interaction between PEs can be defined by a set of instructions, which return a value depending on the neighbor's state. That value is then used as an input to the FSM. This paper studies an instruction set reduced to a single instruction called ''IfAny $q$'' that tests IfAny of the neighbors has a given state $q$. This instruction puts a minimal requirement on hardware: there is no need for addressing channels, communication can be done by local radio broadcasting. An IfAny machine $A$ running on a network tailored for a specific computational task can be executed in parallel on an IfAny medium whose network is fixed and reflects the locality in space. The execution involves an embedding of $A$'s network, and a transformation of $A$'s FSM, adding a 3 states register. We analyse the example of $A$ realizing the addition of $n$ binary numbers. With a carefully chosen network embedding, the resulting parallel execution is optimal in time and space with respect to VLSI complexity. This work demonstrates that IfAny machines can be seen as a rudimentary programming method for computing media. It represents a first step of our long term project which is to realize general purpose parallel computation on a computing medium

Genetic synthesis of boolan neural networks with a cell rewriting developmental process

Communication presentee a : 'First Annual Somptation and Neural Systems Meeting'-CNS *92, San Francisco (USA), 26-31 July 1992SIGLEAvailable at INIST (FR), Document Supply Service, under shelf-number : RM 1013 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc