381 research outputs found
Enhancing feature selection with a novel hybrid approach incorporating genetic algorithms and swarm intelligence techniques
Computing advances in data storage are leading to rapid growth in large-scale datasets. Using all features increases temporal/spatial complexity and negatively influences performance. Feature selection is a fundamental stage in data preprocessing, removing redundant and irrelevant features to minimize the number of features and enhance the performance of classification accuracy. Numerous optimization algorithms were employed to handle feature selection (FS) problems, and they outperform conventional FS techniques. However, there is no metaheuristic FS method that outperforms other optimization algorithms in many datasets. This motivated our study to incorporate the advantages of various optimization techniques to obtain a powerful technique that outperforms other methods in many datasets from different domains. In this article, a novel combined method GASI is developed using swarm intelligence (SI) based feature selection techniques and genetic algorithms (GA) that uses a multi-objective fitness function to seek the optimal subset of features. To assess the performance of the proposed approach, seven datasets have been collected from the UCI repository and exploited to test the newly established feature selection technique. The experimental results demonstrate that the suggested method GASI outperforms many powerful SI-based feature selection techniques studied. GASI obtains a better average fitness value and improves classification performance
Deterministic Approximation of a Stochastic Imitation Dynamics with Memory
We provide results of a deterministic approximation for non-Markovian
stochastic processes modeling finite populations of individuals who recurrently
play symmetric finite games and imitate each other according to payoffs. We
show that a system of delay differential equations can be obtained as the
deterministic approximation of such a non-Markovian process. We also show that
if the initial states of stochastic process and the corresponding deterministic
model are close enough, then the trajectory of stochastic process stays close
to that of the deterministic model up to any given finite time horizon with a
probability exponentially approaching one as the population size increases. We
use this result to obtain that the lower bound of the population size on the
absorption time of the non-Markovian process is exponentially increasing.
Additionally, we obtain the replicator equations with distributed and discrete
delay terms as examples and analyze how the memory of individuals can affect
the evolution of cooperation in a two-player symmetric Snow-drift game. We
investigate the stability of the evolutionary stable state of the game when
agents have the memory of past population states, and implications of these
results are given for the stochastic model.Comment: 23 pages, 2 figures one of which includes 4 subfigure
Identifying the hazard boundary of ML-enabled autonomous systems using cooperative co-evolutionary search
In Machine Learning (ML)-enabled autonomous systems (MLASs), it is essential to identify the hazard boundary of ML Components (MLCs) in the MLAS under analysis. Given that such boundary captures the conditions in terms of MLC behavior and system context that can lead to hazards, it can then be used to, for example, build a safety monitor that can take any predefined fallback mechanisms at runtime when reaching the hazard boundary. However, determining such hazard boundary for an ML component is challenging. This is due to the problem space combining system contexts (i.e., scenarios) and MLC behaviors (i.e., inputs and outputs) being far too large for exhaustive exploration and even to handle using conventional metaheuristics, such as genetic algorithms. Additionally, the high computational cost of simulations required to determine any MLAS safety violations makes the problem even more challenging. Furthermore, it is unrealistic to consider a region in the problem space deterministically safe or unsafe due to the uncontrollable parameters in simulations and the non-linear behaviors of ML models (e.g., deep neural networks) in the MLAS under analysis. To address the challenges, we propose MLCSHE (ML Component Safety Hazard Envelope), a novel method based on a Cooperative Co-Evolutionary Algorithm (CCEA), which aims to tackle a high-dimensional problem by decomposing it into two lower-dimensional search subproblems. Moreover, we take a probabilistic view of safe and unsafe regions and define a novel fitness function to measure the distance from the probabilistic hazard boundary and thus drive the search effectively. We evaluate the effectiveness and efficiency of MLCSHE on a complex Autonomous Vehicle (AV) case study. Our evaluation results show that MLCSHE is significantly more effective and efficient compared to a standard genetic algorithm and random search
Adaptive dynamical networks
It is a fundamental challenge to understand how the function of a network is related to its structural organization. Adaptive dynamical networks represent a broad class of systems that can change their connectivity over time depending on their dynamical state. The most important feature of such systems is that their function depends on their structure and vice versa. While the properties of static networks have been extensively investigated in the past, the study of adaptive networks is much more challenging. Moreover, adaptive dynamical networks are of tremendous importance for various application fields, in particular, for the models for neuronal synaptic plasticity, adaptive networks in chemical, epidemic, biological, transport, and social systems, to name a few. In this review, we provide a detailed description of adaptive dynamical networks, show their applications in various areas of research, highlight their dynamical features and describe the arising dynamical phenomena, and give an overview of the available mathematical methods developed for understanding adaptive dynamical networks
Evolution from the ground up with Amee – From basic concepts to explorative modeling
Evolutionary theory has been the foundation of biological research for about a century
now, yet over the past few decades, new discoveries and theoretical advances have rapidly
transformed our understanding of the evolutionary process. Foremost among them are
evolutionary developmental biology, epigenetic inheritance, and various forms of evolu-
tionarily relevant phenotypic plasticity, as well as cultural evolution, which ultimately led
to the conceptualization of an extended evolutionary synthesis. Starting from abstract
principles rooted in complexity theory, this thesis aims to provide a unified conceptual
understanding of any kind of evolution, biological or otherwise. This is used in the second
part to develop Amee, an agent-based model that unifies development, niche construction,
and phenotypic plasticity with natural selection based on a simulated ecology. Amee
is implemented in Utopia, which allows performant, integrated implementation and
simulation of arbitrary agent-based models. A phenomenological overview over Amee’s
capabilities is provided, ranging from the evolution of ecospecies down to the evolution
of metabolic networks and up to beyond-species-level biological organization, all of
which emerges autonomously from the basic dynamics. The interaction of development,
plasticity, and niche construction has been investigated, and it has been shown that while
expected natural phenomena can, in principle, arise, the accessible simulation time and
system size are too small to produce natural evo-devo phenomena and –structures. Amee thus can be used to simulate the evolution of a wide variety of processes
Operational research:methods and applications
Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order
WiFi-Based Human Activity Recognition Using Attention-Based BiLSTM
Recently, significant efforts have been made to explore human activity recognition (HAR) techniques that use information gathered by existing indoor wireless infrastructures through WiFi signals without demanding the monitored subject to carry a dedicated device. The key intuition is that different activities introduce different multi-paths in WiFi signals and generate different patterns in the time series of channel state information (CSI). In this paper, we propose and evaluate a full pipeline for a CSI-based human activity recognition framework for 12 activities in three different spatial environments using two deep learning models: ABiLSTM and CNN-ABiLSTM. Evaluation experiments have demonstrated that the proposed models outperform state-of-the-art models. Also, the experiments show that the proposed models can be applied to other environments with different configurations, albeit with some caveats. The proposed ABiLSTM model achieves an overall accuracy of 94.03%, 91.96%, and 92.59% across the 3 target environments. While the proposed CNN-ABiLSTM model reaches an accuracy of 98.54%, 94.25% and 95.09% across those same environments
Identifying the Hazard Boundary of ML-enabled Autonomous Systems Using Cooperative Co-Evolutionary Search
In Machine Learning (ML)-enabled autonomous systems (MLASs), it is essential
to identify the hazard boundary of ML Components (MLCs) in the MLAS under
analysis. Given that such boundary captures the conditions in terms of MLC
behavior and system context that can lead to hazards, it can then be used to,
for example, build a safety monitor that can take any predefined fallback
mechanisms at runtime when reaching the hazard boundary. However, determining
such hazard boundary for an ML component is challenging. This is due to the
problem space combining system contexts (i.e., scenarios) and MLC behaviors
(i.e., inputs and outputs) being far too large for exhaustive exploration and
even to handle using conventional metaheuristics, such as genetic algorithms.
Additionally, the high computational cost of simulations required to determine
any MLAS safety violations makes the problem even more challenging.
Furthermore, it is unrealistic to consider a region in the problem space
deterministically safe or unsafe due to the uncontrollable parameters in
simulations and the non-linear behaviors of ML models (e.g., deep neural
networks) in the MLAS under analysis. To address the challenges, we propose
MLCSHE (ML Component Safety Hazard Envelope), a novel method based on a
Cooperative Co-Evolutionary Algorithm (CCEA), which aims to tackle a
high-dimensional problem by decomposing it into two lower-dimensional search
subproblems. Moreover, we take a probabilistic view of safe and unsafe regions
and define a novel fitness function to measure the distance from the
probabilistic hazard boundary and thus drive the search effectively. We
evaluate the effectiveness and efficiency of MLCSHE on a complex Autonomous
Vehicle (AV) case study. Our evaluation results show that MLCSHE is
significantly more effective and efficient compared to a standard genetic
algorithm and random search
Operational Research: Methods and Applications
Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes
Corporate strategies for sustainable development and adoption of new technologies
Technological advancements might have positive or negative impacts on sustainability. It’s essential to understand the adoption of these technologies to achieve better sustainability. The United Nations 2030 Agenda and the associated SDGs have been promoted as tools suitable to alleviate poverty, protect Planet Earth, and contribute to worldwide prosperity (UN, 2015; Tsalis, 2020). But governments alone cannot achieve sustainable development; they must be supported by the private sector, which plays a colossal role in advancing and achieving the SDGs. Specifically, the private sector can integrate the ‘green’ principles into their corporate strategies. This integration depends on, and requires, an effective approach to green development and the knowledge generation of SDGs as embedded in the companies’ functions, values, and day-to-day operations. The papers in this special issue investigate the role of corporate strategies for sustainable green development and knowledge generation in the implementation of the SDGs or principles by Asian and Eastern European companies from Malaysia, Vietnam, Indonesia, Emirates, Zimbabwe and Russia. Hence, there is a need to expand the research in further studies to gauge the contribution of corporate strategies towards the achievement of the SDGs in a wider group of countries. These further studies could also focus on a comparative cross-country analysis to provide insights into how institutional differences among countries influence the implementation and achievement of the SDGs. In addition, there is also a need to understand the role of other corporate strategies, including integrated reporting and long-term value, in the achievement of the SDGs. It is a matter of great importance for companies to explain how businesses create value for their key stakeholders in the long term by implementing the SDGs.
The insights drawn from this special issue contribute to the existing literature and provide valuable practical information for practitioners, policymakers, and developers. Practitioners can rely on the insights provided in this special issue to make informed decisions that consider both the short-term and long-term impacts of technology solutions and their adoption in organizations. They need to consider the opportunities and challenges associated with technology adoption and develop plans to mitigate the negative impacts and maximize the positive effects of technology adoption. Additionally, policymakers can use the findings of the eight papers to establish policies and regulations that encourage the adoption of sustainable technologies that serve society while minimizing the negative impacts on the environment, economy, and the general public. Further, developers can consider the barriers identified in the analysis to develop more effective solutions. They can also incorporate sustainable practices into the development process to ensure their technologies align with sustainable development principles
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