In this article evolutionary computation (EC) is considered as a kind of nature-inspired computing (NIC) paradigm. EC not only has great effect on the development of computing methods from structure to process, but also has great effect on many aspects of our society as a ubiquitous or general computational thinking. EC is still one of the best choices for problem solving among all methods when people face more and more complex problems.

In this article in the ACM Ubiquity symposium on evolutionary computation Max H. Garzon presents reflections on the connections between evolutionary computation, natural computation, and current definitions of computer science. The primary aim and result is a more coherent, comprehensive and modern definition of computer science.

During the past 35 years the evolutionary computation research community has been studying properties of evolutionary algorithms. Many claims have been made---these varied from a promise of developing an automatic programming methodology to solving virtually any optimization problem (as some evolutionary algorithms are problem independent). However, the most important claim was related to applicability of evolutionary algorithms to solving very complex business problems, i.e. problems, where other techniques failed. So it might be worthwhile to revisit this claim and to search for evolutionary algorithm-based software applications, which were accepted by businesses and industries. In this article Zbigniew Michalewicz attempts to identify reasons for the mismatch between the efforts of hundreds of researchers who make substantial contribution to the field of evolutionary computation and the number of real-world applications, which are based on concepts of evolutionary algorithms.