6,600 research outputs found
Evolutionary Dynamic Optimization and Machine Learning
Evolutionary Computation (EC) has emerged as a powerful field of Artificial
Intelligence, inspired by nature's mechanisms of gradual development. However,
EC approaches often face challenges such as stagnation, diversity loss,
computational complexity, population initialization, and premature convergence.
To overcome these limitations, researchers have integrated learning algorithms
with evolutionary techniques. This integration harnesses the valuable data
generated by EC algorithms during iterative searches, providing insights into
the search space and population dynamics. Similarly, the relationship between
evolutionary algorithms and Machine Learning (ML) is reciprocal, as EC methods
offer exceptional opportunities for optimizing complex ML tasks characterized
by noisy, inaccurate, and dynamic objective functions. These hybrid techniques,
known as Evolutionary Machine Learning (EML), have been applied at various
stages of the ML process. EC techniques play a vital role in tasks such as data
balancing, feature selection, and model training optimization. Moreover, ML
tasks often require dynamic optimization, for which Evolutionary Dynamic
Optimization (EDO) is valuable. This paper presents the first comprehensive
exploration of reciprocal integration between EDO and ML. The study aims to
stimulate interest in the evolutionary learning community and inspire
innovative contributions in this domain
Investigating Decision Support Techniques for Automating Cloud Service Selection
The compass of Cloud infrastructure services advances steadily leaving users
in the agony of choice. To be able to select the best mix of service offering
from an abundance of possibilities, users must consider complex dependencies
and heterogeneous sets of criteria. Therefore, we present a PhD thesis proposal
on investigating an intelligent decision support system for selecting Cloud
based infrastructure services (e.g. storage, network, CPU).Comment: Accepted by IEEE Cloudcom 2012 - PhD consortium trac
Task Runtime Prediction in Scientific Workflows Using an Online Incremental Learning Approach
Many algorithms in workflow scheduling and resource provisioning rely on the
performance estimation of tasks to produce a scheduling plan. A profiler that
is capable of modeling the execution of tasks and predicting their runtime
accurately, therefore, becomes an essential part of any Workflow Management
System (WMS). With the emergence of multi-tenant Workflow as a Service (WaaS)
platforms that use clouds for deploying scientific workflows, task runtime
prediction becomes more challenging because it requires the processing of a
significant amount of data in a near real-time scenario while dealing with the
performance variability of cloud resources. Hence, relying on methods such as
profiling tasks' execution data using basic statistical description (e.g.,
mean, standard deviation) or batch offline regression techniques to estimate
the runtime may not be suitable for such environments. In this paper, we
propose an online incremental learning approach to predict the runtime of tasks
in scientific workflows in clouds. To improve the performance of the
predictions, we harness fine-grained resources monitoring data in the form of
time-series records of CPU utilization, memory usage, and I/O activities that
are reflecting the unique characteristics of a task's execution. We compare our
solution to a state-of-the-art approach that exploits the resources monitoring
data based on regression machine learning technique. From our experiments, the
proposed strategy improves the performance, in terms of the error, up to
29.89%, compared to the state-of-the-art solutions.Comment: Accepted for presentation at main conference track of 11th IEEE/ACM
International Conference on Utility and Cloud Computin
Discovering three-dimensional patterns in real-time from data streams: An online triclustering approach
Triclustering algorithms group sets of coordinates of 3-dimensional datasets. In this paper,
a new triclustering approach for data streams is introduced. It follows a streaming scheme
of learning in two steps: offline and online phases. First, the offline phase provides a sum mary model with the components of the triclusters. Then, the second stage is the online
phase to deal with data in streaming. This online phase consists in using the summary
model obtained in the offline stage to update the triclusters as fast as possible with genetic
operators. Results using three types of synthetic datasets and a real-world environmental
sensor dataset are reported. The performance of the proposed triclustering streaming algo rithm is compared to a batch triclustering algorithm, showing an accurate performance
both in terms of quality and running timesMinisterio de Ciencia, Innovación y Universidades TIN2017-88209-C
Lot Streaming in Different Types of Production Processes: A PRISMA Systematic Review
At present, any industry that wanted to be considered a vanguard must be willing to improve itself, developing innovative techniques to generate a competitive advantage against its direct competitors. Hence, many methods are employed to optimize production processes, such as Lot Streaming, which consists of partitioning the productive lots into overlapping small batches to reduce the overall operating times known as Makespan, reducing the delivery time to the final customer. This work proposes carrying out a systematic review following the PRISMA methodology to the existing literature in indexed databases that demonstrates the application of Lot Streaming in the different production systems, giving the scientific community a strong consultation tool, useful to validate the different important elements in the definition of the Makespan reduction objectives and their applicability in the industry. Two hundred papers were identified on the subject of this study. After applying a group of eligibility criteria, 63 articles were analyzed, concluding that Lot Streaming can be applied in different types of industrial processes, always keeping the main objective of reducing Makespan, becoming an excellent improvement tool, thanks to the use of different optimization algorithms, attached to the reality of each industry.This work was supported by the Universidad Tecnica de Ambato (UTA) and their Research and Development Department (DIDE) under project CONIN-P-256-2019, and SENESCYT by grants “Convocatoria Abierta 2011” and “Convocatoria Abierta 2013”
- …