8,719 research outputs found
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
Structural and parametric uncertainties in full Bayesian and graphical lasso based approaches: beyond edge weights in psychological networks
Uncertainty over model structures poses a challenge
for many approaches exploring effect strength parameters at
system-level. Monte Carlo methods for full Bayesian model
averaging over model structures require considerable computational
resources, whereas bootstrapped graphical lasso and its
approximations offer scalable alternatives with lower complexity.
Although the computational efficiency of graphical lasso based
approaches has prompted growing number of applications, the
restrictive assumptions of this approach are frequently ignored,
such as its lack of coping with interactions. We demonstrate
using an artificial and a real-world example that full Bayesian
averaging using Bayesian networks provides detailed estimates
through posterior distributions for structural and parametric
uncertainties and it is a feasible alternative, which is routinely
applicable in mid-sized biomedical problems with hundreds of
variables. We compare Bayesian estimates with corresponding
frequentist quantities from bootstrapped graphical lasso using
pairwise Markov Random Fields, discussing also their interpretational
differences. We present results using synthetic data from
an artificial model and using the UK Biobank data set to explore
a psychopathological network centered around depression (this
research has been conducted using the UK Biobank Resource
under Application Number 1602)
Information fusion between knowledge and data in Bayesian network structure learning
Bayesian Networks (BNs) have become a powerful technology for reasoning under
uncertainty, particularly in areas that require causal assumptions that enable
us to simulate the effect of intervention. The graphical structure of these
models can be determined by causal knowledge, learnt from data, or a
combination of both. While it seems plausible that the best approach in
constructing a causal graph involves combining knowledge with machine learning,
this approach remains underused in practice. We implement and evaluate 10
knowledge approaches with application to different case studies and BN
structure learning algorithms available in the open-source Bayesys structure
learning system. The approaches enable us to specify pre-existing knowledge
that can be obtained from heterogeneous sources, to constrain or guide
structure learning. Each approach is assessed in terms of structure learning
effectiveness and efficiency, including graphical accuracy, model fitting,
complexity, and runtime; making this the first paper that provides a
comparative evaluation of a wide range of knowledge approaches for BN structure
learning. Because the value of knowledge depends on what data are available, we
illustrate the results both with limited and big data. While the overall
results show that knowledge becomes less important with big data due to higher
learning accuracy rendering knowledge less important, some of the knowledge
approaches are actually found to be more important with big data. Amongst the
main conclusions is the observation that reduced search space obtained from
knowledge does not always imply reduced computational complexity, perhaps
because the relationships implied by the data and knowledge are in tension
Socio-hydrological modelling: a review asking “why, what and how?”
Interactions between humans and the environment are occurring on a scale that
has never previously been seen; the scale of human interaction with the water
cycle, along with the coupling present between social and hydrological
systems, means that decisions that impact water also impact people. Models
are often used to assist in decision-making regarding hydrological systems,
and so in order for effective decisions to be made regarding water resource
management, these interactions and feedbacks should be accounted for in
models used to analyse systems in which water and humans interact. This paper
reviews literature surrounding aspects of socio-hydrological modelling. It
begins with background information regarding the current state of
socio-hydrology as a discipline, before covering reasons for modelling and
potential applications. Some important concepts that underlie
socio-hydrological modelling efforts are then discussed, including ways of
viewing socio-hydrological systems, space and time in modelling, complexity,
data and model conceptualisation. Several modelling approaches are described,
the stages in their development detailed and their applicability to
socio-hydrological cases discussed. Gaps in research are then highlighted to
guide directions for future research. The review of literature suggests that
the nature of socio-hydrological study, being interdisciplinary, focusing on
complex interactions between human and natural systems, and dealing with long
horizons, is such that modelling will always present a challenge; it is,
however, the task of the modeller to use the wide range of tools afforded to
them to overcome these challenges as much as possible. The focus in
socio-hydrology is on understanding the human–water system in a holistic
sense, which differs from the problem solving focus of other water management
fields, and as such models in socio-hydrology should be developed with a view
to gaining new insight into these dynamics. There is an essential choice that
socio-hydrological modellers face in deciding between representing individual
system processes or viewing the system from a more abstracted level and
modelling it as such; using these different approaches has implications for
model development, applicability and the insight that they are capable of
giving, and so the decision regarding how to model the system requires
thorough consideration of, among other things, the nature of understanding
that is sought
A Bayesian network model to explore practice change by smallholder rice farmers in Lao PDR
© 2018 A Bayesian Network model has been developed that synthesizes findings from concurrent multi-disciplinary research activities. The model describes the many factors that impact on the chances of a smallholder farmer adopting a proposed change to farming practices. The model, when applied to four different proposed technologies, generated insights into the factors that have the greatest influence on adoption rates. Behavioural motivations for change are highly dependent on farmers' individual viewpoints and are also technology dependent. The model provides a boundary object that provides an opportunity to engage experts and other stakeholders in discussions about their assessment of the technology adoption process, and the opportunities, barriers and constraints faced by smallholder farmers when considering whether to adopt a technology
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