Measuring and Promoting Diversity in Evolutionary Algorithms
While divergence of character is a cornerstone of the Darwinian theory of natural evolution, the lack of divergence is an oppressive burden of artificial evolution. The problem has been sometimes labeled with the oxymoron "premature convergence", implying that the algorithms would converge to better solutions if prevented from exploiting strongly attractive local optima. Different names have been given by different researchers, and different effects have been observed on different paradigms. Almost all deleterious.
The goal of "Measuring and Promoting Diversity in Evolutionary Algorithms" (MPDEA) workshops is to show the current lines of research in the fields of both diversity promotion and measurement, as illustrated by their own authors. We would be happy if MPDEA would give its attendees the opportunity to dive into this important and open problem, first providing them with background information and eventually enabling them to present their bleeding-edge ideas.
The MPDEA's call for papers also included a call for references, where we asked scholars to pinpoint any interesting contribution already published. Thanks to the replies, we are proud to add an updated bibliography on diversity promotion to the material of the workshop.