Hybrid approaches to optimization and machine learning methods: a systematic literature review

Abstract Notably, real problems are increasingly complex and require sophisticated models and algorithms capable of quickly dealing with large data sets and finding optimal solutions. However, there is no perfect method or algorithm; all of them have some limitations that can be mitigated or eliminated by combining the skills of different methodologies. In this way, it is expected to develop hybrid algorithms that can take advantage of the potential and particularities of each method (optimization and machine learning) to integrate methodologies and make them more efficient. This paper presents an extensive systematic and bibliometric literature review on hybrid methods involving optimization and machine…

• MD
  Ministério da Ciência, Tecnologia e Ensino Superior

  Awards: LA/P/0007/2021, UIDB/05757/2020, UIDP/05757/2020
• IP
  Instituto Politécnico de Bragança
• FP
  Fundação para a Ciência e a Tecnologia

  Awards: SFRH/BD/07427/2021, UIDP/05757/2020, LA/P/0007/2021, UIDB/00319/2020, UIDB/05757/2020