Postdoctoral Fellow in Computational Biology
McGill University
We are looking for a postdoctoral researcher to join our efforts in understanding the gene regulatory basis of cell identity and human diseases.
See the full job description on jobRxiv: https://jobrxiv.org/job/mcgill-university-27778-postdoctoral-fellow-in-computational-biology/?feed_id=82319
#bioinformatics #computatio...
https://jobrxiv.org/job/mcgill-university-27778-postdoctoral-fellow-in-computational-biology/?feed_id=82319
Postdoctoral Fellow in Computational Biology
McGill University
We are looking for a postdoctoral researcher to join our efforts in understanding the gene regulatory basis of cell identity and human diseases.
See the full job description on jobRxiv: https://jobrxiv.org/job/mcgill-university-27778-postdoctoral-fellow-in-computational-biology/?feed_id=82319
#bioinformatics #computatio...
https://jobrxiv.org/job/mcgill-university-27778-postdoctoral-fellow-in-computational-biology/?feed_id=82319
Postdoctoral Fellow in Computational Biology
McGill University
We are looking for a postdoctoral researcher to join our efforts in understanding the gene regulatory basis of cell identity and human diseases.
See the full job description on jobRxiv: https://jobrxiv.org/job/mcgill-university-27778-postdoctoral-fellow-in-computational-biology/?feed_id=81346
#bioinformatics #computatio...
https://jobrxiv.org/job/mcgill-university-27778-postdoctoral-fellow-in-computational-biology/?feed_id=81346
We propose the Interactive Constrained MAP-Elites, a quality-diversity solution for game content generation, implemented as a new feature of the Evolutionary Dungeon Designer: a mixed-initiative co-creativity tool for designing dungeons. The feature uses the MAP-Elites algorithm, an illumination algorithm that segregates the population among several cells depending on their scores with respect to different behavioral dimensions. Users can flexibly and dynamically alternate between these dimensions anytime, thus guiding the evolutionary process in an intuitive way, and then incorporate suggestions produced by the algorithm in their room designs. At the same time, any modifications performed by the human user will feed back into MAP-Elites, closing a circular workflow of constant mutual inspiration. This paper presents the algorithm followed by an in-depth analysis of its behaviour, with the aims of evaluating the expressive range of all possible dimension combinations in several scenarios, as well as discussing their influence in the fitness landscape and in the overall performance of the mixed-initiative procedural content generation.
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