Abstract
Quality Diversity algorithms (QD) evolve a set of high-performing
phenotypes that each behaves as differently as possible. However,
current algorithms are all elitist, which make them unable to cope
with stochastic fitness functions and behavior evaluations. In fact,
many of the promising applications of QD algorithms, for instance,
games and robotics, are stochastic. Here we propose two new extensions to the QD-algorithm MAP-Elites — adaptive sampling and
drifting-elites — and demonstrate empirically that these extensions
increase the quality of solutions in a noisy artificial test function
and the behavioral diversity in a 2D bipedal walker environment.
phenotypes that each behaves as differently as possible. However,
current algorithms are all elitist, which make them unable to cope
with stochastic fitness functions and behavior evaluations. In fact,
many of the promising applications of QD algorithms, for instance,
games and robotics, are stochastic. Here we propose two new extensions to the QD-algorithm MAP-Elites — adaptive sampling and
drifting-elites — and demonstrate empirically that these extensions
increase the quality of solutions in a noisy artificial test function
and the behavioral diversity in a 2D bipedal walker environment.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the Genetic and Evolutionary Computation Conference Companion : GECCO '19 |
| Number of pages | 2 |
| Publisher | Association for Computing Machinery |
| Publication date | 2019 |
| Pages | 121-122 |
| DOIs | |
| Publication status | Published - 2019 |