Hybridation of Bayesian networks and evolutionary algorithms for multi-objective optimization in an integrated product design and project management context

A better integration of preliminary product design and project management processes at early steps of system design is nowadays a key industrial issue. Therefore, the aim is to make firms evolve from classical sequential approach (first product design the project design and management) to new integrated approaches. In this paper, a model for integrated product/project optimization is first proposed which allows taking into account simultaneously decisions coming from the product and project managers. However, the resulting model has an important underlying complexity, and a multi-objective optimization technique is required to provide managers with appropriate scenarios in a reasonable amount of time. The proposed approach is based on an original evolutionary algorithm called evolutionary algorithm oriented by knowledge (EAOK). This algorithm is based on the interaction between an adapted evolutionary algorithm and a model of knowledge (MoK) used for giving relevant orientations during the search process. The evolutionary operators of the EA are modified in order to take into account these orientations. The MoK is based on the Bayesian Network formalism and is built both from expert knowledge and from individuals generated by the EA. A learning process permits to update probabilities of the BN from a set of selected individuals. At each cycle of the EA, probabilities contained into the MoK are used to give some bias to the new evolutionary operators. This method ensures both a faster and effective optimization, but it also provides the decision maker with a graphic and interactive model of knowledge linked to the studied project. An experimental platform has been developed to experiment the algorithm and a large campaign of tests permits to compare different strategies as well as the benefits of this novel approach in comparison with a classical EA

Clinical review: Imaging in ischaemic stroke – implications for acute management

Imaging has become a cornerstone of stroke management, translating pathophysiological knowledge to everyday decision-making. Plain computed tomography is widely available and remains the standard for initial assessment: the technique rules out haemorrhage, visualizes the occluding thrombus and identifies early tissue hypodensity and swelling, which have different implications for thrombolysis. Based on evidence from positron emission tomography (PET), however, multimodal imaging is increasingly advocated. Computed tomography perfusion and angiography provide information on the occlusion site, on recanalization and on the extent of salvageable tissue. Magnetic resonance-based diffusion-weighted imaging (DWI) has exquisite sensitivity for acute ischaemia, however, and there is increasingly robust evidence that DWI combined with perfusion-weighted magnetic resonance imaging (PWI) and angiography improves functional outcome by selecting appropriate patients for thrombolysis (small DWI lesion but large PWI defect) and by ruling out those who would receive no benefit or might be harmed (very large DWI lesion, no PWI defect), especially beyond the 3-hour time window. Combined DWI–PWI also helps predict malignant oedema formation and therefore helps guide selection for early brain decompression. Finally, DWI–PWI is increasingly used for patient selection in therapeutic trials. Although further methodological developments are awaited, implementing the individual pathophysiologic diagnosis based on multimodal imaging is already refining indications for thrombolysis and offers new opportunities for management of acute stroke patients