Diversity visualization in evolutionary algorithms

Evolutionary Algorithms (EAs) are well-known nature-inspired optimization methods. Diversity is an essenial aspect of each EA. It describes the variability of organisms in population. The lack of diversity is common problem – diversity should be preserved in order to evade local extremes (premature convergence). Niching algorithms are modifications of classical EAs. Niching is based on dividing the population into separate subpopulations it spreads the organisms effectively all over the search space and hence making the overall population diverse. Using niching methods also requires setting of their parameters, which can be very difficult. This paper presents a novel way of diversity visualization based on physical system simulation. This visualization is helpful when designing and tuning niching algorithms but it has also other uses. The visualization will be presented on NEAT the evolutionary algorithm which optimizes both the topology and the parameters of neural networks.