The goal of bi-objective optimization is to find a small set of good compromise solutions. A common problem for bi-objective evolutionary algorithms is the following subset selection problem (SSP): Given n solutions P ⊂ R 2 in the objective space, select k solutions P * from P that optimize an indicator function. In the hypervolume SSP we want to select k points P * that maximize the hypervolume indicator Ihyp(P * , r) for some reference point r ∈ R 2 . Similarly, the ε-indicator SSP aims at selecting k points P * that minimize the ε-indicator Ieps(P * , R) for some reference set R ⊂ R 2 of size m (which can be R = P ).We first present a new algorithm for the hypervolume SSP with runtime O(n (k + log n)). Our second main result is a new algorithm for the ε-indicator SSP with runtime O(n log n + m log m).Both results improve the current state of the art runtimes by a factor of (nearly) n and make the problems tractable for new applications. Preliminary experiments confirm that the theoretical results translate into substantial empirical runtime improvements.

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Succinct data structures are becoming increasingly popular in big data processing applications due to their low memory consumption. However, a feature that is currently lacking from most implementations of succinct data structures is dynamism. In this paper we design, implement, and test a general framework that allows for practical dynamic succinct structures. Firstly, a key component of our approach is careful memory management, which is often overlooked in the succinct data structures literature. Most succinct data structures allocate and deallocate relatively small data blocks each time a modify, insert, or delete operation occurs. We demonstrate experimentally that the space cost of neglecting memory management can be over 25% for dynamic data structures of this type. Secondly, using our memory management approach, we describe implementations of compressed modifiable bit vectors, and extended compressed random access memory (recently proposed by Jansson, Sadakane, and Sung [ICALP 2012]). Finally, we implement and test our data structures using several popular compression libraries, and both synthetic data (for the compressed modifiable bit vector) and a real-world temporal graph (for the extended compressed random access memory). Our data structures provide an easy to use interface that allow standard algorithms (in our example, breadth-first search in a graph) to be