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DCR Event-Reachability via Genetic Algorithms

机译:通过遗传算法实现DCR事件可达性

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In declarative process models, a process is described as a set of rules as opposed to a set of permitted flows. Oftentimes, such rule-based notations are more concise than their flow-based cousins; however, that conciseness comes at a cost: It requires computation to work out which flows are in fact allowed by the rules of the process. In this paper, we present an algorithm to solve the Reachability problem for the declarative Condition Response (DCR) graphs notation: the problem, given a DCR graph and an activity, say "Payout reimbursement", is to find a flow allowed by the graph that ends with the execution of that task. Existing brute-force solutions to this problem are generally unhelpful already at medium-sized graphs. Here we present a genetic algorithm solving Reachability. We evaluate this algorithm on a selection of DCR graphs, both artificial and from industry, and find that the genetic algorithm with one exception outperforms the best known brute-force solution on both whether a path is found and how quickly it is found.
机译:在声明性流程模型中,流程被描述为一组规则,而不是一组允许的流程。通常,这种基于规则的表示法比其基于流程的表亲更为简洁。但是,简洁是有代价的:需要进行计算才能确定出流程规则实际上允许哪些流程。在本文中,我们提出一种算法来解决声明性条件响应(DCR)图表示法的可到达性问题:给定DCR图和活动(例如“付款偿还”)的问题是要找到以该任务的执行结束的图。对于中型图形,现有的解决该问题的强力解决方案通常已无济于事。在这里,我们提出一种解决可达性的遗传算法。我们在选择的DCR图上(无论是人工的还是来自行业的)对这种算法进行了评估,发现在发现路径和发现路径方面,有一个例外的遗传算法的性能优于最著名的蛮力解决方案。

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