This paper presents a proposal for the extraction of association rules called G3PARM (Grammar-Guided Genetic Programming for Association Rule Mining) that makes the knowledge extracted more expressive and flexible. This algorithm allows a context-free grammar to be adapted and applied to each specific problem or domain and eliminates the problems raised by discretization. This proposal keeps the best individuals (those that exceed a certain threshold of support and confidence) obtained with the passing of generations in an auxiliary population of fixed size n. G3PARM obtains solutions within specified time limits and does not require the large amounts of memory that the exhaustive search algorithms in the field of association rules do. Our approach is compared to exhaustive search (Apriori and FP-Growth) and genetic (QuantMiner and ARMGA) algorithms for mining association rules and performs an analysis of the mined rules. Finally, a series of experiments serve to contrast the scalability of our algorithm. The proposal obtains a small set of rules with high support and confidence, over 90 and 99% respectively. Moreover, the resulting set of rules closely satisfies all the dataset instances. These results illustrate that our proposal is highly promising for the discovery of association rules in different types of datasets.

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Web service based applications often invoke services provided by thirdparties in their workflow. The Quality of Service (QoS) provided by the invoked supplier can be expressed in terms of the Service Level Agreement specifying the values contracted for particular aspects like cost or throughput, among others.In this scenario, intelligent systems can support the engineer to scrutinise the service market in order to select those candidates that best fit with the expected composition focusing on different QoS aspects. This search problem, a.k.a.QoS-aware web service composition, is characterised by the presence of many diverse QoS properties to be simultaneously optimised from a multi-objective perspective. Nevertheless, as the number of QoS properties considered during the design phase increases and a larger number of decision factors come into play, it becomes more difficult to find the most suitable candidate solutions, so more sophisticated techniques are required to explore and return diverse, competitive alternatives. With this aim, this paper explores the suitability of many-objective evolutionary algorithms for addressing the binding problem of web services on the basis of a real-world benchmark with 9 QoS properties. A complete comparative study demonstrates that these techniques, never before * Corresponding author. Tel.: +34 957 21 26 60Email addresses: aramirez@uco.es (Aurora Ramírez), japarejo@us.es (José Antonio Parejo), jrromero@uco.es (José Raúl Romero), sergiosegura@us.es (Sergio Segura), aruiz@us.es (Antonio Ruiz-Cortés) Preprint submitted to Expert Systems with ApplicationsOctober 31, 2016 applied to this problem, can achieve a better trade-off between all the QoS properties, or even promote specific QoS properties while keeping high values for the rest. In addition, this search process can be performed within a reasonable computational cost, enabling its adoption by intelligent and decision-support systems in the field of service oriented computation.

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This paper treats the first approximation to the extraction of association rules by employing ant programming, a technique that has recently reported very promising results in mining classification rules. In particular, two different algorithms are presented, both guided by a context-free grammar that defines the search space, specifically suited to association rule mining. The first proposal follows a single-objective approach in which a novel fitness function is used to evaluate the individuals mined. In contrast, the second algorithm considers individual evaluation from a Pareto-based point of view, measuring the confidence and support of the rules mined and assigning them a ranking fitness. Both algorithms are verified over 16 varied data sets, comparing their results to other association rule mining algorithms from several paradigms such as exhaustive search, genetic algorithms, and genetic programming. The results obtained are very promising, and they indicate that ant programming is a good technique for the association task of data mining, lacking of the drawbacks that exhaustive methods present.

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