11,273 research outputs found
Genetic algorithms
Genetic algorithms are mathematical, highly parallel, adaptive search procedures (i.e., problem solving methods) based loosely on the processes of natural genetics and Darwinian survival of the fittest. Basic genetic algorithms concepts are introduced, genetic algorithm applications are introduced, and results are presented from a project to develop a software tool that will enable the widespread use of genetic algorithm technology
Performance-based control system design automation via evolutionary computing
This paper develops an evolutionary algorithm (EA) based methodology for computer-aided control system design (CACSD)
automation in both the time and frequency domains under performance satisfactions. The approach is automated by efficient
evolution from plant step response data, bypassing the system identification or linearization stage as required by conventional
designs. Intelligently guided by the evolutionary optimization, control engineers are able to obtain a near-optimal ‘‘off-thecomputer’’
controller by feeding the developed CACSD system with plant I/O data and customer specifications without the need of
a differentiable performance index. A speedup of near-linear pipelineability is also observed for the EA parallelism implemented on
a network of transputers of Parsytec SuperCluster. Validation results against linear and nonlinear physical plants are convincing,
with good closed-loop performance and robustness in the presence of practical constraints and perturbations
Meta-heuristic algorithms in car engine design: a literature survey
Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy. Due to their great potential in solving difficult optimization problems, meta-heuristic algorithms have found their way into automobile engine design. There are different optimization problems arising in different areas of car engine management including calibration, control system, fault diagnosis, and modeling. In this paper we review the state-of-the-art applications of different meta-heuristic algorithms in engine management systems. The review covers a wide range of research, including the application of meta-heuristic algorithms in engine calibration, optimizing engine control systems, engine fault diagnosis, and optimizing different parts of engines and modeling. The meta-heuristic algorithms reviewed in this paper include evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system
Neural Networks for Modeling and Control of Particle Accelerators
We describe some of the challenges of particle accelerator control, highlight
recent advances in neural network techniques, discuss some promising avenues
for incorporating neural networks into particle accelerator control systems,
and describe a neural network-based control system that is being developed for
resonance control of an RF electron gun at the Fermilab Accelerator Science and
Technology (FAST) facility, including initial experimental results from a
benchmark controller.Comment: 21 p
Optimizing genetic algorithm strategies for evolving networks
This paper explores the use of genetic algorithms for the design of networks,
where the demands on the network fluctuate in time. For varying network
constraints, we find the best network using the standard genetic algorithm
operators such as inversion, mutation and crossover. We also examine how the
choice of genetic algorithm operators affects the quality of the best network
found. Such networks typically contain redundancy in servers, where several
servers perform the same task and pleiotropy, where servers perform multiple
tasks. We explore this trade-off between pleiotropy versus redundancy on the
cost versus reliability as a measure of the quality of the network.Comment: 9 pages, 5 figure
Genetic algorithms with immigrants and memory schemes for dynamic shortest path routing problems in mobile ad hoc networks
This article is posted here with permission of IEEE - Copyright @ 2010 IEEEIn recent years, the static shortest path (SP) problem has been well addressed using intelligent optimization techniques, e.g., artificial neural networks, genetic algorithms (GAs), particle swarm optimization, etc. However, with the advancement in wireless communications, more and more mobile wireless networks appear, e.g., mobile networks [mobile ad hoc networks (MANETs)], wireless sensor networks, etc. One of the most important characteristics in mobile wireless networks is the topology dynamics, i.e., the network topology changes over time due to energy conservation or node mobility. Therefore, the SP routing problem in MANETs turns out to be a dynamic optimization problem. In this paper, we propose to use GAs with immigrants and memory schemes to solve the dynamic SP routing problem in MANETs. We consider MANETs as target systems because they represent new-generation wireless networks. The experimental results show that these immigrants and memory-based GAs can quickly adapt to environmental changes (i.e., the network topology changes) and produce high-quality solutions after each change.This work was supported by the Engineering
and Physical Sciences Research Council of U.K. underGrant EP/E060722/
Exploring tradeoffs in pleiotropy and redundancy using evolutionary computing
Evolutionary computation algorithms are increasingly being used to solve
optimization problems as they have many advantages over traditional
optimization algorithms. In this paper we use evolutionary computation to study
the trade-off between pleiotropy and redundancy in a client-server based
network. Pleiotropy is a term used to describe components that perform multiple
tasks, while redundancy refers to multiple components performing one same task.
Pleiotropy reduces cost but lacks robustness, while redundancy increases
network reliability but is more costly, as together, pleiotropy and redundancy
build flexibility and robustness into systems. Therefore it is desirable to
have a network that contains a balance between pleiotropy and redundancy. We
explore how factors such as link failure probability, repair rates, and the
size of the network influence the design choices that we explore using genetic
algorithms.Comment: 10 pages, 6 figure
Oil and Gas flow Anomaly Detection on offshore naturally flowing wells using Deep Neural Networks
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceThe Oil and Gas industry, as never before, faces multiple challenges. It is being impugned for being
dirty, a pollutant, and hence the more demand for green alternatives. Nevertheless, the world still has
to rely heavily on hydrocarbons, since it is the most traditional and stable source of energy, as opposed
to extensively promoted hydro, solar or wind power. Major operators are challenged to produce the
oil more efficiently, to counteract the newly arising energy sources, with less of a climate footprint,
more scrutinized expenditure, thus facing high skepticism regarding its future. It has to become
greener, and hence to act in a manner not required previously.
While most of the tools used by the Hydrocarbon E&P industry is expensive and has been used for
many years, it is paramount for the industry’s survival and prosperity to apply predictive maintenance
technologies, that would foresee potential failures, making production safer, lowering downtime,
increasing productivity and diminishing maintenance costs. Many efforts were applied in order to
define the most accurate and effective predictive methods, however data scarcity affects the speed
and capacity for further experimentations. Whilst it would be highly beneficial for the industry to invest
in Artificial Intelligence, this research aims at exploring, in depth, the subject of Anomaly Detection,
using the open public data from Petrobras, that was developed by experts.
For this research the Deep Learning Neural Networks, such as Recurrent Neural Networks with LSTM
and GRU backbones, were implemented for multi-class classification of undesirable events on naturally
flowing wells. Further, several hyperparameter optimization tools were explored, mainly focusing on
Genetic Algorithms as being the most advanced methods for such kind of tasks.
The research concluded with the best performing algorithm with 2 stacked GRU and the following
vector of hyperparameters weights: [1, 47, 40, 14], which stand for timestep 1, number of hidden units
47, number of epochs 40 and batch size 14, producing F1 equal to 0.97%.
As the world faces many issues, one of which is the detrimental effect of heavy industries to the
environment and as result adverse global climate change, this project is an attempt to contribute to
the field of applying Artificial Intelligence in the Oil and Gas industry, with the intention to make it
more efficient, transparent and sustainable
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