DIGNEA: A tool to generate diverse and discriminatory instances for optimisation domains

• Alejandro Marrero Díaz ; Eduardo Manuel Segredo González ; Coromoto León Hernández ; Emma Hart ^[1]
  1. [1] Edinburgh Napier University

     Edinburgh Napier University

     Reino Unido

     
• Localización: Actas del XVI Congreso Español de Metaheurísticas, Algoritmos Evolutivos y Bioinspirados: (MAEB 2025) 28-30 de mayo, Donostia/San Sebastián / coord. por Leticia Hernando Rodríguez , Josu Ceberio Uribe , Jon Vadillo Jueguen, 2025, ISBN 978-84-1319-656-5, págs. 318-321
• Idioma: inglés
• Enlaces
  • Texto Completo Libro
• Resumen

  • This paper presents DIGNEA, a novel tool designed to generate diverse and dis- criminatory instances for various optimisation domains. DIGNEA utilizes an evolutionary algorithm-based framework that incorporates novelty search tech- niques to ensure the generated instances are not only varied but also useful for assessing different solvers. The tool, written in C++, is available as a repository and a Docker image, making it easily adaptable to different domains and solver types. Recently, a Python version has been released to facilitate the adoption by the research community. An application to the Knapsack Problem is showcased, demonstrating its effectiveness in generating meaningful test instances.