Real-world problems are often comprised of many objectives and require solutions that carefully trade-off between them. Current approaches to many-objective optimization often require challenging assumptions, like knowledge of the importance/difficulty of objectives in a weighted-sum single-objective paradigm, or enormous populations to overcome the curse of dimensionality in multi-objective Pareto optimization. Combining elements from Many-Objective Evolutionary Algorithms and Quality Diversity algorithms like MAP-Elites, we propose Many-objective Optimization via Voting for Elites (MOVE). MOVE maintains a map of elites that perform well on different subsets of the objective functions. On a 14-objective image-neuroevolution problem, we demonstrate that MOVE is viable with a population of as few as 50 elites and outperforms a naive single-objective baseline. We find that the algorithm's performance relies on solutions jumping across bins (for a parent to produce a child that is elite for a different subset of objectives). We suggest that this type of goal-switching is an implicit method to automatic identification of stepping stones or curriculum learning. We comment on the similarities and differences between MOVE and MAP-Elites, hoping to provide insight to aid in the understanding of that approach $\unicode{x2013}$ and suggest future work that may inform this approach's use for many-objective problems in general.

S. Tenier, A. Napoli, X. Polanco and Y.Toussaint (2006) With the proliferation of news articles from thousands of developed an automatic WebPages semantic annotation different sources now available on the Web, summarization system. The objective is to classify pages concerning teams of such information is becoming increasingly important. of research, in order to be able to determine for example Considering the large number of news source (for who works where, on what and with whom (use of examples, BBC, Reuters, CNN…), every day, thousands of ontology of the domain). It consists, first, of the articles are produced in the entire world concerning a given identification of the syntactic structure characterizing the event.