What Is NeuroEvolution of Augmenting Topologies (NEAT)?

In a world where neural networks are the go-to solution for everything from speech recognition to image recognition, it is no surprise that researchers are looking for ways to improve their performance. NeuroEvolution of Augmenting Topologies or NEAT is often described as a genetic solution for improving neural networks. The NEAT concept can be used to provide a new model for selecting typologies for a neural network and for initializing weights. NeuroEvolution of Augmenting Topologies (NEAT) is based on an Evolution Strategies (ES) algorithm developed by John Holland in 1992. The basic idea behind ES is that it mimics Darwinian evolution by introducing random mutations into an initial population and then selecting individuals from this population based on their fitness levels. In this case, the fitness levels are determined by how well they produce desired outputs given particular inputs. While ES may sound simple enough, it has some drawbacks: firstly, it relies on the user having prior knowledge of what the resulting output should look like; secondly, it requires knowledge about what the inputs are going to be; thirdly, it requires knowledge about how much change should be introduced into each individual at each step; finally, it requires knowledge about how many iterations will take place before. The network is a very complex system; engineers need to consider many factors when designing it. Topology is one of them, and it refers to the layout of connections between different parts of a network. In most cases, engineers train the network to learn about input weights through training data which means that they use training data from other parts of the network to define how connections work among different parts. This can be challenging because it requires detailed knowledge about all other parts to create effective connections. However, with NEAT, this is not necessary anymore because it offers a radically new way of defining topology by simply learning about input weights through training data.