Neurocontroller Analysis via Evolutionary Network Minimization

This study presents a new evolutionary network minimization (ENM) algorithm. Neurocontroller minimization is beneficial for finding small parsimonious networks that permit a better understanding of their workings. The ENM algorithm is specifically geared to an evolutionary agents setup, as it does not require any explicit supervised training error, and is very easily incorporated in current evolutionary algorithms. ENM is based on a standard genetic algorithm with an additional step during reproduction in which synaptic connections are irreversibly eliminated. It receives as input a successfully evolved neurocontroller and aims to output a pruned neurocontroller, while maintaining the original fitness level. The small neurocontrollers produced by ENM provide upper bounds on the neurocontroller size needed to perform a given task successfully, and can provide for more effcient hardware implementations.

• Type: Journal
• Year: 2006
• Where: ALIFE
• Authors: Zohar Ganon, Alon Keinan, Eytan Ruppin
• Keywords: Algorithm, ALIFE 2006, ALIFE 2007, Evolutionary Network Minimization, Small Parsimonious Networks