Evolutionary Computation Visualization: ECvis

This paper presents a visualisation tool (ECvis) that aids the development of population based numerical optimisation algorithms such as genetic algorithms and differential evolution. The tool provides a simple interface with three modes: A Density mode that allows the user to quickly view the distribution and density of the population throughout the fitness and search space for high dimensional problems. This provides the ability to quickly establish where the population is clustering, which can indicate potential local and global minima; A Statistical mode that allows the user to visualise the individuals that have statistical significance in the population and their location in the search space; A Ranges mode that provides the user with a windowed average of the minimum, maximum and median value of each parameter of the population and whether the range of each parameter has changed since the previous window. This allows the user to see whether the population is exhibiting exploration or exploitation properties, as well as convergence properties such as the variance of each parameter. As examples of the usefulness of ECvis, two well known, high dimensional functions are optimised using differential evolution with ECvis being used to provide information on the performance of the optimisation.

Automated summary

This paper presents a visualization tool called ECvis that assists in the development of population-based numerical optimization algorithms. The tool provides a simple interface with three modes: Density mode, Statistical mode, and Ranges mode. Through examples, the usefulness of ECvis in optimizing high dimensional functions using differential evolution is demonstrated.