Managing Diversity and Many Objectives in Evolutionary Design

by Sheikh Faishal Basher

Supervisor: Brian Ross

MAP-Elite solutions after a run.

A new approach to evolving a diversity of high quality solutions for problems having many objectives is presented. Mouret and Clune’s MAP-Elites algorithm has been proposed as a way to evolve an assortment of diverse solutions to a problem. We extend MAP-Elites in a number of ways. We introduce into MAP-elites the many-objective strategy called sum-of-ranks, which enables problems with many objectives (4 and more) to be considered in the MAP. We also enhance MAPElites by extending it with multiple solutions per cell (the original MAP-Elites saves only a single solution per cell). Different ways of selecting cell members for reproduction are also considered. We test the new MAP-Elites strategies on the evolutionary art application of image generation. Using procedural textures, genetic programming is used with upwards of 15 light-weight image features to guide fitness. The goal is to evolve images that share image features with a given target image. Our experiments show that the new MAP-Elites algorithms produce a large number of diverse solutions, which can be competitive in quality to those from standard GP runs. The technique can be used in a wide variety of applications.

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Copyright (C) 2021 Sheikh Faishal Basher.

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