7409552
Swarm optimisation as a new tool for data mining
This paper proposes the use of particle swarm optimisers as a tool for data mining. To evaluate its usefulness, we empirically compare the performance of three variants of the particle optimiser with another evolutionary algorithm, namely a genetic algorithm, in rule discovery for classification tasks. Such tasks are considered core tools for decision support systems in a widespread area, ranging from the industry, commerce, military and scientific fields. The data sources used here for experimental testing are commonly used and considered as a de facto standard for rule discovery algorithms reliability ranking. The results obtained in these domains seem to indicate that particle swarm optimisers are competitive with other evolutionary techniques, and can be successfully applied to more demanding problem domains.
10782860
OrgSwarm - A Particle Swarm Model of Organizational Adaptation
This study extends the particle swarm metaphor to include the domain of organizational adaptation. A simulation model, OrgSwarm, is constructed in order to examine the impact of strategic inertia on the adaptive potential of a population of organizations. The key finding is that a degree of strategic inertia, in the presence of an election mechanism, can assist rather than hamper adaption of the population.
6828563
A Particle Swarm Data Miner
This paper describes the implementation of Data Mining tasks using Particle Swarm Optimisers. The object of our research has been to apply such algorithms to classification rule discovery. Results, concerning accuracy and speed performance, were empirically compared with another evolutionary algorithm, namely a Genetic Algorithm and with J48 – a Java implementation of C4.5. The data sets used for experimental testing have already been widely used and proven reliable for testing other Data Mining algorithms. The obtained results seem to indicate that Particle Swarm Optimisers are competitive with other evolutionary techniques, and could come to be successfully applied to more demanding problem domains.
2626702
A Particle Swarm Model of Organizational Adaptation
This study introduces the particle swarm metaphor to the domain of organizational adaptation. A simulation model (OrgSwarm) is constructed to examine the impact of strategic inertia, in the presence of errorful assessments of future payoffs to potential strategies, on the adaptation of the strategic fitness of a population of organizations. The results indicate that agent (organization) uncertainty as to the payoffs of potential strategies has the affect of lowering average payoffs obtained by a population of organizations. The results also indicate that a degree of strategic inertia, in the presence of an election mechanism, assists rather than hampers adaptive efforts in static and slowly changing strategic environments.
191535
Polymorphy and Hybridization in Genetically Programmed Networks
In this paper we discuss the polymorphic abilities of a new distributed representation for genetic programming, called Genetically Programmed Networks. These are inspired in a common structure in natural complex adaptive systems, where system functionality frequently emerges from the combined functionality of simple computational entities, densely interconnected for information exchange. A Genetically Programmed Network can be evolved into a distributed program, a rule based system or a neural network with simple adjustments to the evolutionary algorithm. The space of possible network topologies can also be easily controlled. This allows the fast exploration of various search spaces thus increasing the possibility of finding a (or a better) solution. Experimental results are presented to support our claims.
5929323
Agent-based modelling of product invention
This study describes a novel simulation model of the process of product invention. Invention is conceptualized as a process of directed evolutionary adaptation, on a landscape of product design possibilities, by a population of profit-seeking agents (inventors). The simulation experiments examine the sensitivity of the rate of advance in product fitness to the choice of search heuristics employed by inventors. The key finding of the experiments is that if search heuristics are confined to those which are rooted in past experience, or to heuristics which merely generate variety, limited product advance occurs. Notable product fitness advance only occurs when inventor's expectations as to the relative fitness of potential product inventions are incorporated into the model of invention. The results demonstrate the importance of human direction and expectations in invention. They also support the importance of formal product / project evaluation procedures in organizations, and the importance of market information when inventing new products.
11562802
A Decision Support System for IST Academic Information
This article describes the Decision Support System (DSS) for Academic Information being developed at Instituto Superior Tecnico, the Engineering School of the Technical University of Lisbon. In Portuguese, this project has been given the acronym SADIA (Sistema de Apoio a Decisao da Informacao Academica). This paper focuses on the early phases of the DSS development process, i.e., the business requirements definition and the dimensional modelling. First, we show how the business requirements of the School drive the definition of the DSS dimensional model. Second, we detail the logical dimensional model for a selected business process, the IST Student Admission process. Third, the corresponding physical design decisions are reported. The results obtained from the three phases were successfully validated by business users.
15079292
Measuring similarity between collection of values
In this paper, we propose a set of similarity metrics for manipulating collections of values occuring in XML documents.
 Following the data model presented in TAX algebra, we treat an XML element as a labeled ordered rooted tree. Consider that XML nodes can be either atomic, i.e, they may contain single values such as short character strings, date, etc, or complex, i.e., nested structures that contain other nodes, we propose two types of similarity metrics: MAVs, for atomic nodes and MCVs, for complex nodes. In the first case, we suggest the use of several application domain dependent metrics. In the second case, we define metrics for complex values that are structure dependent, and can be distinctly applied for it and collections of values. We also present experiments showing the effectiveness of our method.
12706002
Computer-Assisted Discrepancy Management - A Case Study in Research Transfer to Industry
This paper reports on the “repackaging” and transfer to industry of research results in ViewpointBased Requirements Engineering (VBRE). Our experience, as indicated here, is a particular case of the problems and solutions that small research groups can find in their transfer from theoretical work to practical application. In particular, starting with a formal approach for discrepancy management, we first developed a “light-weight version” of VBRE and empirically validated their most important claims. Then we developed a software tool (discRman) that encapsulates the main points of the approach and, finally, partner organizations used it in small projects. We think that this “theoretical-empirical-light-weight-tool” sequence can be applied in similar situations, especially by small research groups seeking for transfer of their results to industry. ACM Classification: D.2.1 (Software Engineering – Requirements/Specifications – Methodologies, Tools), D.2.8 (Software Engineering – Metrics – Performance measures), D.2.9 (Software Engineering – Management – Productivity)
15673525
Genetic Algorithms for Component Analysis
Adapted waveform analysis is used extensively in audio, speech, and video coding. In this context, the basic problem is to find out the best representation for a given signal. The strategy is to decompose the signal into time-frequency atoms. The main analysis tool is the expansion of the signal in or-thonormal bases whose elements have good time-frequency localizations. We show that genetic algorithms can be exploited to search libraries of bases for atomic pattern recognition in noisy signals. The proposed algorithm generates best basis decomposition trees through the evolution of well-adapted genetic sequences. Two new types of constraint operators are introduced to guarantee that valid genetic sequences are generated. The approach behaves as an atomic analyzer for signal processing. A test environment is proposed to assess recognition of time-frequency atoms in signal spaces.
8086424
Dynamic evolution in workflow management systems
The environments where workflow management systems are typically used in are constantly changing. It is nearly impossible to foresee, at business process design stage, all the combinations of tasks needed to achieve the process' goals. Workflow management systems capable of supporting dynamic changes on executing instances in a flexible manner are thereby demanded. Former approaches have proposed a solution to this issue based on workflow type versioning and workflow instance migration. However, the operations that handle the modification of workflow types as well as the migration algorithm for workflow instances are tied to a particular representation of workflow types. This means that whenever the representation changes, the migration algorithms and the modification operations have to change accordingly. This is a current problem due to the uprising of different specifications for workflow definition languages, which are far away from stabilizing. In this paper, we address this problem by clearly decoupling the modification operations of workflow types from the internal structures and algorithms responsible for the instances' migration. In this manner we enable the use of different workflow definition languages while keeping the same internal structures and migration algorithms.