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Concept

Evolutionary computation

Related publications (29)

Biophysically accurate and machine learning-based surrogate models to optimize neuroprosthesis design and operation

Simone Romeni

Electrical stimulation of the nervous system has emerged as a promising assistive technology in case of many injuries and illnesses across various parts of the nervous system. In particular, the invasive neuromodulation of the peripheral nervous system see ...

EPFL2024

The Two Times Problem: Where Is the Problem?

Michael Herzog

Gruber et al. (2022) offered a framework how to explain "Physical time within human time", solving the 'two times problem: Here, I am asking whether such a problem exists at all. To question the question, I will appeal to neurobiological, evolutionary, and ...

Brill2024

Design and experimental investigation of a herringbone grooved gas bearing supported turbocharger

Jürg Alexander Schiffmann, Wanhui Liu

The design of an oil free turbocharger supported on herringbone grooved gas bearing was formulated as a multi-objective problem, which was solved by coupling a reduced order parametric model for gas bearing supported rotors with an evolutionary algorithm. ...

2023

Byzantine-Resilient Learning Beyond Gradients: Distributing Evolutionary Search

Rachid Guerraoui, Matteo Monti, Andrei Kucharavy

Modern machine learning (ML) models are capable of impressive performances. However, their prowess is not due only to the improvements in their architecture and training algorithms but also to a drastic increase in computational power used to train them.|S ...

Assoc Computing Machinery2023

Playing Brains: The Ethical Challenges Posed by Silicon Sentience and Hybrid Intelligence in DishBrain

Georg Michael Joachim Starke

The convergence of human and artificial intelligence is currently receiving considerable scholarly attention. Much debate about the resulting Hybrid Minds focuses on the integration of artificial intelligence into the human brain through intelligent brain- ...

2023

A Framework for Autonomic Computing for In Situ Imageomics

Nina Marion Aurélia Van Tiel

In situ imageomics is a new approach to study ecological, biological and evolutionary systems wherein large image and video data sets are captured in the wild and machine learning methods are used to infer biological traits of individual organisms, animal ...

The Institute of Electrical and Electronics Engineers, Inc2023

PSGL-1 is an evolutionarily conserved antiviral restriction factor

Ying Liu

The arms race between viruses and their hosts shaped the evolutionary history and the genome composition of both parties. Restriction factors are the first-line antiviral effectors encoded by the host genomes and are often conserved through evolution to pr ...

AMER SOC MICROBIOLOGY2023

Adaptive Symplectic Model Order Reduction Of Parametric Particle-Based Vlasov-Poisson Equation

Jan Sickmann Hesthaven, Cecilia Pagliantini, Nicolò Ripamonti

. High-resolution simulations of particle-based kinetic plasma models typically require a high number of particles and thus often become computationally intractable. This is exacerbated in multi-query simulations, where the problem depends on a set of para ...

AMER MATHEMATICAL SOC2023

Cellularization across eukaryotes: Conserved mechanisms and novel strategies

Omaya Pierre Dudin

Many eukaryotes form multinucleated cells during their devel-opment. Some cells persist as such during their lifetime, others choose to cleave each nucleus individually using a specialized cytokinetic process known as cellularization. What is cellula-rizat ...

CURRENT BIOLOGY LTD2023

Design and computational optimization of compliance-matching aortic grafts

Nikolaos Stergiopoulos, Vasiliki Bikia, Georgios Rovas

Introduction: Synthetic vascular grafts have been widely used in clinical practice for aortic replacement surgery. Despite their high rates of surgical success, they remain significantly less compliant than the native aorta, resulting in a phenomenon calle ...

FRONTIERS MEDIA SA2023

Universal and adaptive methods for robust stochastic optimization

Ali Kavis

Within the context of contemporary machine learning problems, efficiency of optimization process depends on the properties of the model and the nature of the data available, which poses a significant problem as the complexity of either increases ad infinit ...

EPFL2023

Genetic Optimization of Homogeneous Catalysts

Anne-Clémence Corminboeuf, Rubén Laplaza Solanas, Simone Gallarati

We present the NaviCatGA package, a versatile genetic algorithm capable of optimizing molecular catalyst structures using well-suited fitness functions to achieve a set of targeted properties. The flexibility and generality of this tool are validated and d ...

2022

A universal workflow for creation, validation and generalization of detailed neuronal models

Henry Markram, Werner Alfons Hilda Van Geit, Srikanth Ramaswamy, Alexis Arnaudon, Christian Andreas Rössert, Mustafa Anil Tuncel, Maria Reva, Tanguy Pierre Louis Damart, Darshan Mandge

Detailed single neuron modeling is widely used to study neuronal functions. While cellular and functional diversity across the mammalian cortex is vast, most of the available computational tools are dedicated to the reproduction of a small set of specific ...

Cambridge2023

Optimization methods for collaborative learning

Sai Praneeth Reddy Karimireddy

A traditional machine learning pipeline involves collecting massive amounts of data centrally on a server and training models to fit the data. However, increasing concerns about the privacy and security of user's data, combined with the sheer growth in the ...

EPFL2021

Seeking Quality Diversity in Evolutionary Co-design of Morphology and Control of Soft Tensegrity Modular Robots

Dario Floreano, Davide Zappetti, Giovanni Iacca, Davide Zambrano

Designing optimal soft modular robots is difficult, due to non-trivial interactions between morphology and controller. Evolutionary algorithms (EAs), combined with physical simulators, represent a valid tool to overcome this issue. In this work, we investi ...

Association for Computing Machinery2021

Emergence of diauxie as an optimal growth strategy under resource allocation constraints in cellular metabolism

Vassily Hatzimanikatis, Pierre Guy Rémy Salvy

Diauxie, or the sequential consumption of carbohydrates in bacteria such as Escherichia coli, has been hypothesized to be an evolutionary strategy which allows the organism to maximize its instantaneous specific growth-giving the bacterium a competitive ad ...

National Academy of Sciences2021

Modified Joint Channel-and-Data Estimation for One-Bit Massive MIMO

Saeed Saadatnejad, Mohammadhossein Bahari, Mahdi Amiri

Centralized and cloud computing-based network architectures are the promising tracks of future communication systems where a large scale compute power can be virtualized for various algorithms. These architectures rely on high-performance communication lin ...

IEEE2021

Federated Learning Over Wireless Networks: Convergence Analysis and Resource Allocation

Vincent Gramoli

There is an increasing interest in a fast-growing machine learning technique called Federated Learning (FL), in which the model training is distributed over mobile user equipment (UEs), exploiting UEs' local computation and training data. Despite its advan ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

Phenomenology and scaling of optimal flapping wing kinematics

Karen Ann J Mulleners, Alexander Gehrke

Biological flapping wing fliers operate efficiently and robustly in a wide range of flight conditions and are a great source of inspiration to engineers. The unsteady aerodynamics of flapping wing flight are dominated by large-scale vortical structures tha ...

IOP PUBLISHING LTD2021

Analytical Computation of Power Grids’ Sensitivity Coefficients with Voltage-Dependent Injections

Mario Paolone, Sherif Alaa Salaheldin Fahmy

With the increasing need of real-time regulation in power systems, grid-aware-control-frameworks are relying more often on sensitivity coefficients (SCs) to formulate and efficiently solve optimal control problems. As known, SCs are the derivatives of cont ...

IEEE2021