Evolving modular genetic regulatory networks with a recursive, top-down approach

Evolving Modular Genetic Regulatory Networks

In this paper we introduce a system that combines ontogenetic development and artificial evolution to automatically design robots in a physics-based, virtual environment. Through lesion experiments on the evolved agents, we demonstrate that the evolved genetic regulatory networks from successful evolutionary runs are more modular than those obtained from unsuccessful runs. I. Literature Review ...

Evolving Modular Geneti Regulatory Networks

Evolving Modular Recursive Sorting Algorithms

A fundamental issue in evolutionary learning is the definition of the solution representation language. We present the application of Object Oriented Genetic Programming to the task of coevolving general recursive sorting algorithms along with their primitive representation alphabet. We report the computational effort required to evolve target solutions and provide a comparison between crossove...

Evolving Biological Clocks using Genetic Regulatory Networks

We study the evolvability and dynamics of artificial genetic regulatory networks (GRNs), as active control systems, realizing simple models of biological clocks that have evolved to respond to periodic environmental stimuli of various kinds with appropriate periodic behaviors. GRN models may differ in the evolvability of expressive regulatory dynamics. A new class of artificial GRNs with an evo...

Modular and Efficient Top-Down Parsing for Ambiguous Left-Recursive Grammars

In functional and logic programming, parsers can be built as modular executable specifications of grammars, using parser combinators and definite clause grammars respectively. These techniques are based on top-down backtracking search. Commonly used implementations are inefficient for ambiguous languages, cannot accommodate left-recursive grammars, and require exponential space to represent par...