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.
Instead of considering the population of NSLC as the result of the algorithms, @cite_36 suggested to consider the novelty archive as the result. Indeed, the aim of the novelty archive is to keep track of the different solution types that are encountered during the process, and thus to cover as much as possible of the entire descriptor space. Therefore, the novelty archive can be considered as a collection of diverse solutions on its own. However, the solutions are stored in the collection without considering their quality: as soon as a new type of solution is found, it is added to archive. While this procedure allows the archive to cover the entire spectrum of the possible solutions, in the original version of NSLC only the first encountered solution of each type is added to the archive. This implies that when finding a better solution for a solution type already present in the archive, this solution is not added to the archive. This mechanism prevents the archive from improving over time.
@cite_36
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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 (QD) optimization, has been introduced, and contrasts with classic algorithms. Instead of searching for a single solution, QD 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. First, we present a unifying framework of QD optimization algorithms that covers the two main algorithms of this family (multidimensional 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. Second, we propose algorithms with a new selection mechanism for QD 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 QD algorithms on three different experimental scenarios. The Web is ephemeral. Many resources have representations that change over time, and many of those representations are lost forever. A lucky few manage to reappear as archived resources that carry their own URIs. For example, some content management systems maintain version pages that reect a frozen prior state of their changing resources. Archives recurrently crawl the web to obtain the actual representation of resources, and subsequently make those available via special-purpose archived resources. In both cases, the archival copies have URIs that are protocolwise disconnected from the URI of the resource of which they represent a prior state. Indeed, the lack of temporal capabilities in the most common Web protocol, HTTP, prevents getting to an archived resource on the basis of the URI of its original. This turns accessing archived resources into a signicant discovery challenge for both human and software agents, which typically involves following a multitude of links from the original to the archival resource, or of searching archives for the original URI. This paper proposes the protocol-based Memento solution to address this problem, and describes a proof-of-concept experiment that includes major servers of archival content, including Wikipedia and the Internet Archive. The Memento solution is based on existing HTTP capabilities applied in a novel way to add the temporal dimension. The result is a framework in which archived resources can seamlessly be reached via the URI of their original: protocol-based time travel for the Web. Search algorithms for Pareto optimization are designed to obtain multiple solutions, each offering a different trade-off of the problem objectives. To make the different solutions available at the end of an algorithm run, procedures are needed for storing them, one by one, as they are found. In a simple case, this may be achieved by placing each point that is found into an "archive" which maintains only nondominated points and discards all others. However, even a set of mutually nondominated points is potentially very large, necessitating a bound on the archive's capacity. But with such a bound in place, it is no longer obvious which points should be maintained and which discarded; we would like the archive to maintain a representative and well-distributed subset of the points generated by the search algorithm, and also that this set converges. To achieve these objectives, we propose an adaptive archiving algorithm, suitable for use with any Pareto optimization algorithm, which has various useful properties as follows. It maintains an archive of bounded size, encourages an even distribution of points across the Pareto front, is computationally efficient, and we are able to prove a form of convergence. The method proposed here maintains evenness, efficiency, and cardinality, and provably converges under certain conditions but not all. Finally, the notions underlying our convergence proofs support a new way to rigorously define what is meant by "good spread of points" across a Pareto front, in the context of grid-based archiving schemes. This leads to proofs and conjectures applicable to archive sizing and grid sizing in any Pareto optimization algorithm maintaining a grid-based archive. The Memento protocol provides a uniform approach to query individual web archives. Soon after its emergence, Memento Aggregator infrastructure was introduced that supports querying across multiple archives simultaneously. An Aggregator generates a response by issuing the respective Memento request against each of the distributed archives it covers. As the number of archives grows, it becomes increasingly challenging to deliver aggregate responses while keeping response times and computational costs under control. Ad-hoc heuristic approaches have been introduced to address this challenge and research has been conducted aimed at optimizing query routing based on archive profiles. In this paper, we explore the use of binary, archive-specific classifiers generated on the basis of the content cached by an Aggregator, to determine whether or not to query an archive for a given URI. Our results turn out to be readily applicable and can help to significantly decrease both the number of requests and the overall response times without compromising on recall. We find, among others, that classifiers can reduce the average number of requests by 77 compared to a brute force approach on all archives, and the overall response time by 42 while maintaining a recall of 0.847.