The optimization of functions to find the best solution according to one or several objectives has a central role in many engineering and research fields. Recently, a new family of optimization algorithms, named Quality-Diversity optimization, has been introduced, and contrasts with classic algorithms. Instead of searching for a single solution, Quality-Diversity algorithms are searching for a large collection of both diverse and high-performing solutions. The role of this collection is to cover the range of possible solution types as much as possible, and to contain the best solution for each type. The contribution of this paper is threefold. Firstly, we present a unifying framework of Quality-Diversity optimization algorithms that covers the two main algorithms of this family (Multi-dimensional Archive of Phenotypic Elites and the Novelty Search with Local Competition), and that highlights the large variety of variants that can be investigated within this family. Secondly, we propose algorithms with a new selection mechanism for Quality-Diversity algorithms that outperforms all the algorithms tested in this paper. Lastly, we present a new collection management that overcomes the erosion issues observed when using unstructured collections. These three contributions are supported by extensive experimental comparisons of Quality-Diversity algorithms on three different experimental scenarios.
The first applications of NSLC consisted of evolving both the morphology and the behavior of virtual creatures in order to generate a population containing diverse species, ranging from slow and massive quadrupeds to fast and lightweight unipedal hoppers by comparing velocity only between similar species @cite_42 . In this experiment, the solution descriptor was defined as the height, the mass and the number of active joints, while the quality of the solutions was governed by their walking speed. At the end of the evolutionary process, the population contained 1,000 different species. These results represent the very first step in the direction of generating a collection of diverse and high-performing solutions covering a significant part of the spectrum of possibilities.
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In this paper we introduce a real-time system for action detection. The system uses a small set of robust features extracted from 3D skeleton data. Features are effectively described based on the probability distribution of skeleton data. The descriptor computes a pyramid of sample covariance matrices and mean vectors to encode the relationship between the features. For handling the intra-class variations of actions, such as action temporal scale variations, the descriptor is computed using different window scales for each action. Discriminative elements of the descriptor are mined using feature selection. The system achieves accurate detection results on difficult unsegmented sequences. Experiments on MSRC-12 and G3D datasets show that the proposed system outperforms the state-of-the-art in detection accuracy with very low latency. To the best of our knowledge, we are the first to propose using multi-scale description in action detection from 3D skeleton data.