An Introduction to Genetic Algorithms - Boente

the brain. In a feedforward network, activation spreads only in a forward direction, from the input layer
through the hidden layers to the output layer. Many people have also experimented with "recurrent" networks,
in which there are feedback connections as well as feedforward connections between layers.
Figure 2.16: A schematic diagram of a simple feedforward neural network and the backpropagation process
by which weight values are adjusted.
After activation has spread through a feedforward network, the resulting activation pattern on the output units
encodes the network's "answer" to the input (e.g., a classification of the input pattern as the letter A). In most
applications, the network learns a correct mapping between input and output patterns via a learning algorithm.
Typically the weights are initially set to small random values. Then a set of training inputs is presented
sequentially to the network. In the back−propagation learning procedure (Rumelhart, Hinton, and Williams
1986), after each input has propagated through the network and an output has been produced, a "teacher"
compares the activation value at each output unit with the correct values, and the weights in the network are
adjusted in order to reduce the difference between the network's output and the correct output. Each iteration
of this procedure is called a "training cycle," and a complete pass of training cycles through the set of training
inputs is called a "training epoch." (Typically many training epochs are needed for a network to learn to
successfully classify a given set of training inputs.) This type of procedure is known as "supervised learning,"
since a teacher supervises the learning by providing correct output values to guide the learning process. In
"unsupervised learning" there is no teacher, and the learning system must learn on its own using less detailed
(and sometimes less reliable) environmental feedback on its performance. (For overviews of neural networks
and their applications, see Rumelhart et al. 1986, McClelland et al. 1986, and Hertz, Krogh, and Palmer
1991.)
There are many ways to apply GAs to neural networks. Some aspects that can be evolved are the weights in a
fixed network, the network architecture (i.e., the number of units and their interconnections can change), and
the learning rule used by the network. Here I will describe four different projects, each of which uses a genetic
algorithm to evolve one of these aspects. (Two approaches to evolving network architecture will be
described.) (For a collection of papers on various combinations of genetic algorithms and neural networks, see
Whitley and Schaffer 1992.)
Evolving Weights in a Fixed Network
Chapter 2: Genetic Algorithms in Problem Solving
David Montana and Lawrence Davis (1989) took the first approach—evolving the weights in a fixed network.
That is, Montana and Davis were using the GA instead of back−propagation as a way of finding a good set of
weights for a fixed set of connections. Several problems associated with the back−propagation algorithm (e.g.,
50