Automatic Construction of Parallel Portfolios via Explicit Instance Grouping

Abstract

Simultaneously utilizing several complementary solvers is a simple yet effective strategy for solving computationally hard problems. However, manually building such solver portfolios typically requires considerable domain knowledge and plenty of human effort. As an alternative, automatic construction of parallel portfolios (ACPP) aims at automatically building effective parallel portfolios based on a given problem instance set and a given rich design space. One promising way to solve the ACPP problem is to explicitly group the instances into different subsets and promote a component solver to handle each of them. This paper investigates solving ACPP from this perspective, and especially studies how to obtain a good instance grouping. The experimental results showed that the parallel portfolios constructed by the proposed method could achieve consistently superior performances to the ones constructed by the stateof-the-art ACPP methods, and could even rival sophisticated hand-designed parallel solvers.

Generative Adversarial Construction of Parallel Portfolios

“…The basic ideas of Hydra and ISAC were later adapted to be used in constructing parallel portfolios, thus resulting in two new approaches PARHYDRA and CLUSTERING [17]. Another key approach for constructing parallel portfolios is PCIT [18], which also adopts an instance grouping strategy such as CLUSTERING but will adjust the grouping by transferring instances between subsets in the construction process. Note that how to evaluate candidate portfolios in the construction process depends on the ways of using the resultant portfolios; therefore the latter should be taken into account in the design of an APC approach.…”

“…As mentioned before, currently all investigations on automatic solver construction require that a training set is given, and it is assumed that the training set is a (representative) part of the target use cases. Hence, it is nonsurprising that most of the above approaches were justified on well-investigated computationally hard problems, such as the planning problems [16]; SAT [12], [14], [15], [17], [18]; and TSP [18], since for these problems, there are quite a few benchmark suites. For these approaches, the training set and the test set for empirical studies were usually obtained by randomly and evenly splitting an existing benchmark set into two disjoint sets, such that the training instances can represent the test instances well.…”

“…The second principle concerns the complementarity [14], [15], [17], [18], [62] among the component solvers, which is crucial for the effectiveness of any parallel portfolio. According to (1), the performance of a parallel portfolio on while time spent in this phase not exceeds t I do 13:…”

“…respectively. For detailed information of how these results are derived, we refer the reader to the original articles [17] for PARHYDRA and GLOBAL and [18] for PCIT. Note that for a specific portfolio construction approach, the values of N C , N V , and N I depend on the predefined time budgets t C , t V , and t I , respectively, and for different approaches, t C , t V , and t I could be set differently.…”

“…This is acceptable (and even appealing) since the available computing power has been rapidly becoming much cheaper than before. 1 Indeed, such approaches have been demonstrated to be both practical and effective in cases of constructing sequential solvers [12], [13]; sequential portfolios [14]- [16] (i.e., algorithm portfolios with selectors/scheduling); and parallel portfolios [17], [18].…”