Применение генетического алгоритма для нахождения редакционного расстояния между моделями процессов
Abstract— In this paper a new multi-agent genetic algorithm for multi-objective optimization (MAGAMO) is presented. The algorithm based on the dynamical interaction of synchronized agents which are interdepended genetic algorithms (GAs) having own separate evolutions of their populations. This approach has some similarities with well known “island model” of GA. In both methods is used a migration of individuals from agents (“islands”) to the main process (“continent”). In contrast, the intelligent agents in MAGAMO are able to decompose the dimensions space to form evolutions of subpopulations (instead of distribution of initial population as in the standard “island model”). In the same time, the main (central) process is responsible for the coordination of agents only and their selection according Pareto rules (without evolution). Intelligent agents seek local suboptimal solutions for a global optimization, which will be completed in the result of the interaction of all agents. In the result of this, the amount of needed recalculating the fitness-functions can be significantly reduced. It is especially important for the multi-objective optimization related to a large-scale problem. Besides, the proposed approximating approach allows solving complex optimization problems for real big systems (like an oil company, plants, corporations, etc.).
Process-aware information systems (PAIS) enable developing models for interaction of processes, monitoring accuracy of their execution and checking if they interact with each other properly. PAIS can generate large data logs that contain the information about the interaction of processes in time. Studying PAIS logs with the purpose of data mining and modeling lies within the scope of Process Mining. There is a number of tools developed for Process Mining, including the most ubiquitous ProM, whose functionality is extended by plugins. To perform an object-aware experiment one has to sequentially run multiple plugins. This process becomes extremely time-consuming in the case of large-scale experiments involving a large number of plugins. The paper proposes a concept of DPMine/P language of process modeling and analysis to be implemented in ProM. The language under development aims at joining separate stages of the experiment into a single sequence, that is an experiment model. The implementation of the basic semantics of the language is done through the concept of blocks, ports, connectors and schemes. These items are discussed in detail in the paper, and examples of their use for specific tasks are presented ibid.
In work the developed model of adaptive management by the vertically integrated companies based on the system approach supporting the mechanism of an operational management in a uniform cycle of strategic planning, within the limits of faster time is presented. Thus for a finding of optimum values of operating parameters special algorithms of a class of genetic algorithms are used, neural networks the example of the developed system of adaptive management for the vertically-integrated oil company is etc. presented.
Process mining is a new direction in the field of modeling and analysis of processes, where using information from event logs, describing the history of the system behavior, plays an important role. Methods and approaches used in the process mining are often based on various heuristics, and experiments with large event logs are crucial for the study and comparison of the developed methods and algorithms. Such experiments are very time consuming, so automation of experiments is an important task in the field of process mining. This paper presents the language DPMine developed specifically to describe and carry out experiments on the discovery and analysis of process models. The basic concepts of the DPMine language, as well as principles and mechanisms of its extension are described. Ways of integration of the DPMine language as dynamically loaded components into the VTMine modeling tool are considered. An illustrating example of an experiment to build a fuzzy model of the process discovered from the log data stored in a normalized database is given.
DPMine generic purpose workflow language is rooted in DPMine/P scientific workflow language and a set of plug-ins for ProM which originally were developed for convenient piping of different plug-ins within ProM framework. DPMine/C is a new version of DPMine workflow language and a C++ library. The main language concept was complemented by comprehensive analysis of DPMine/C model execution semantics. This paper also discusses approaches to the block types extension concept relying on development of new block type classes and customization of the model storage subsystem. Finally, we show an approach for implementation of a GUI frontend.