Evolutionary computations are kinds of random searching algorithms derived from natural selection and biological genetic evolution behavior. Evaluating the performance of an algorithm is a fundamental task to track and find the way to improve the algorithm, while visualization technique may play an important act during the process. Based on current existing algorithm performance evaluation criteria and methods, a python-based programming tracking strategy, which employs 2-D graphical library of python matplotlib for online algorithm performance evaluation, is proposed in this paper. Tracking and displaying the performance of genetic algorithm (GA) and the particle swarm optimization (PSO) optimizing two typical numerical benchmark problems are employed for verification and validation.
Results show that the tracking strategy based on Python language for online performance evaluation of evolutionary algorithms is valid, and can be used to help researchers on algorithms' performance evaluation and finding ways to improve it.