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*Correspondence: [email protected] 1Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA 19104–6021, USA Full list of author information is available at the end of the article Editorial Artificial intelligence (AI), a broad field that deals with the ongoing pursuit to render machines capable of performing intelligent tasks, has taken the academic and industrial worlds by storm in a breathtakingly short time span. These days, when you engage in the daily surf of your favorite news website, somemention of AI will probably ensue. Machine learning, currently the most prominent subfield of AI, focuses on algorithms that learn from data, with deep learning—employing artificial neural networks with several hidden layers—being the jewel in the crown. From playing Go to processing radiological images, machine learning’s success and breadth of scope is undeniable. Yet wemustn’t forget that the parent field of AI has birthed many other offspring. In particular, we wish to shine a light on the field of evolutionary computation (EC), which we believe is poised to be “The Next Big Thing”. In EC, core concepts from evolutionary biology—inheritance, random variation, and selection—are harnessed in algorithms that are applied to complex computational problems. The field of EC, whose origins can be traced back to the 1950s and 60s, has come into its own over the past decade. EC techniques have been shown to solve numerous difficult problems fromwidely diverse domains, in particular producing human-competitive machine intelligence [1]. As argued by the authors of this latter paper, “Surpassing humans in the ability to solve complex problems is a grand challenge, with potentially far-reaching, transformative implications.” EC is applicable over a wide range of problem categories, including classification, regression, clustering, design, optimization, planning, and generating computer programs. Moreover, the range of applications for which EC has worked well is staggering, including such disparate domains as antenna design [2], generating winning game strategies [3], automated program improvement [4], and bioinformatics [5]. EC presents many important benefits over popular deep learning methods: