Differential Evolution: A review of more than two decades of research

By Bilal, Millie Pant, Hira Zaheer, Laura García-Hernández, Ajith Abraham. Engineering applications of artificial intelligence, 2020

869 citations recorded by the source corpus.

Abstract Since its inception in 1995, Differential Evolution (DE) has emerged as one of the most frequently used algorithms for solving complex optimization problems. Its flexibility and versatility have prompted several customized variants of DE for solving a variety of real life and test problems. The present study, surveys the near 25 years of existence of DE. In this extensive survey, 283 research articles have been covered and the journey of DE is shown through its basic aspects like population generation, mutation schemes, crossover schemes, variation in parameters and hybridized variants along with various successful applications of DE. This study also provides some key bibliometric indicators like highly cited papers having citations more than 500, publication trend since 1996, journal citations etc. The main aim of the present document is to serve as an extended summary of 25 years of existence of DE, intended for dissemination to interested parties. It is expected that the present survey would generate interest among the new users towards the philosophy of DE and would also guide the experience researchers.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/b146261d0befb47a94c24db4ea4e7eb156a62af3

Citations in the corpus

• Connecting Large Language Models with Evolutionary Algorithms Yields Powerful Prompt Optimizers (2024, neutral): “…regarded evolutionary algorithms (Holland, 1975; 1992; Mitchell, 1998; Mirjalili et al., 2020) and DE has emerged as one of the most widely utilized algorithms for complex optimization challenges since its inception (Storn & Price, 1997; Price, 2013; Das & Suganthan, 2010; Pant et al., 2020).”