Synchronized Oriented Mutations Algorithm for Training Neural Controllers

Abstract

Developing neural controllers for autonomous robotics is a tedious task as the desired state trajectory of the robot is very often not known in advance. This led to the large success of evolutionary algorithm in this field. In this paper we introduce SOMA (Synchronized Oriented Mutations Algorithm), which presents an alternative for rapidly minimizing the parameters characterizing a given individual. SOMA is characterized by its easy implementation and its flexibility: it can use any continuous fitness function and be applied to optimize neural network of diverse topologies using any kind of activation functions. Contrary to evolutionary approach, it is applied on a single individual rather than on a population. Because the procedure is very fast, it allows for rapid screening and selection of good candidates. In this paper, the efficiency of SOMA at training ordered connection feed forward networks on function modeling problem, classification problem and robotic controllers is investigated.