Incremental evolution of neural network architectures for adaptive behavior.

Abstract

This paper describes aspects of our ongoing work in evolving recurrent dynamical arti cial neural networks which act as sensory motor controllers generating adaptive behaviour in arti cial agents We start with a discussion of the rationale for our approach Our approach involves the use of recurrent networks of arti cial neurons with rich dynamics resilience to noise both internal and external and separate excitation and inhibition channels The networks allow arti cial agents simulated or robotic to exhibit adaptive behaviour The complexity of designing networks built from such units leads us to use our own extended form of genetic algorithm which allows for incremental automatic evolution of controller networks Finally we review some of our recent results applying our methods to work with simple visually guided robots The genetic algorithm generates useful network architectures from an initial set of randomly connected networks During evolution uniform noise was added to the activation of each neuron After evolution we studied two evolved networks to see how their performance varied when the noise range was altered Signi cantly we discovered that when the noise was eliminated the performance of the networks degraded the networks use noise to operate e ciently Introduction and Rationale Increasingly practitioners of arti cial neural network research are realising that both the complexity of model neurons and also the styles of network architecture need to be extended beyond those employed in the much cited work of the early s Certainly models such as Hop eld networks or back propagating multi layer perceptrons played an important historical role in making parallel distributed processing an acceptable paradigm of study but if we are to succeed in either understanding biological nervous systems or in building arti cial neural networks which exhibit intelligent behaviour it is likely that we will have to move to more complex models But what form should this complexity take The notion of complexity is often highly subjective and hence problematic We should de nitely avoid introducing unnecessary complications but more importantly we should not be deceived by our own simpli ca tions In arti cial neural network ann modelling simpli cations are made for various reasons Often there are issues of mathematical tractability certain model neurons or network architectures are easier to formally analyse than others In other cases the ease with which the models can be simulated or built in available hardware is an important factor and appropriate simpli cations are made In either case it is important to note that the simpli cation is made for our convenience the ann is easier to construct or understand The problem with this approach is that in using simpli ed models we may actually be making life harder for ourselves as scientists because the tasks we try to make our models perform may by their very nature require greater complexity than is possible without using clever trick techniques or large and unwieldy modular assemblies of simple networks There are two simpli cations which are very common in ann models most models in the literature have very simple or non existent dynamics and arbitrary connectivity is often avoided It is manifestly clear that networks with many feedback connections and delays between units are much more challenging to either analyse simulate or design than are networks such as the common three layer back propagation network Yet for many interesting and important problems feedback and intrinsic dynamics are almost de nitely what is required Furthermore there is ample evidence in the neuroscience literature from most branches of the animal kingdom that biological neural networks exhibit rich dynamical behaviour and exploit feed back connections to great e ect Additionally many ann s are developed purely to transform between representations or encodings which have been formulated by their designers Such networks may be w