Experiences Using Julia for Implementing Multi-objective Evolutionary Algorithms

Julia is a programming language suitable for data analysis and scientific computing that combines simplicity of productivity languages with characteristics of performance-oriented languages. In this paper, we are interested in studying the use of Julia to implement Multi-Objective MetaHeuristics. Concretely, we use the Java-based jMetal framework as a reference support and investigate how Julia could be used to design and develop the component-based architecture for multi-objective evolutionary algorithms that jMetal provides. By using the NSGA-II algorithm as an example, we analyze the advantages and shortcomings of using Julia in this context, including aspects related to reusing jMetal code and a performance comparison.