Optimisation of building form for solar energy utilisation using constrained evolutionary algorithms

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Increasing the scanning speed in Atomic Force Microscopy (AFM) relies on improving the tracking performance in the vertical direction of motion. The lightly damped resonances of piezo-actuators utilized in AFM nano-positioning stages hinder the maximum ach ...

As one of the major threats to women's health worldwide, breast cancer requires early diagnosis and accurate classification, since they are key to optimizing therapeutic interventions and ensuring precise prognosis. Recently, deep learning has demonstrated ...

Deep neural networks (DNNs) are receiving increasing attention in wind power forecasting due to their ability to effectively capture complex patterns in wind data. However, their forecast errors are severely limited by the local optimal weight issue in opt ...

Recently, the diffusion moving-average (D-MA) scheme has been proposed as a way to combat noisy links over adaptive networks. However, the current theoretical results focus on networks with mean-square error costs where the optimal local solution agrees wi ...

Modern optimization is tasked with handling applications of increasingly large scale, chiefly due to the massive amounts of widely available data and the ever-growing reach of Machine Learning. Consequently, this area of research is under steady pressure t ...

This paper offers a new algorithm to efficiently optimize scheduling decisions for dial-a-ride problems (DARPs), including problem variants considering electric and autonomous vehicles (e-ADARPs). The scheduling heuristic, based on linear programming theor ...

The increasing reliance on numerical methods for controlling dynamical systems and training machine learning models underscores the need to devise algorithms that dependably and efficiently navigate complex optimization landscapes. Classical gradient desce ...

Cutting plane methods are a fundamental approach for solving integer linear programs (ILPs). In each iteration of such methods, additional linear constraints (cuts) are introduced to the constraint set with the aim of excluding the previous fractional opti ...

We develop new tools to study landscapes in nonconvex optimization. Given one optimization problem, we pair it with another by smoothly parametrizing the domain. This is either for practical purposes (e.g., to use smooth optimization algorithms with good g ...

In this account, we discuss the use of genetic algorithms in the inverse design process of homogeneous catalysts for chemical transformations. We describe the main components of evolutionary experiments, specifically the nature of the fitness function to o ...

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. ...

Non-convex constrained optimization problems have become a powerful framework for modeling a wide range of machine learning problems, with applications in k-means clustering, large- scale semidefinite programs (SDPs), and various other tasks. As the perfor ...

The sense of touch can provide a robot with a wealth of information about the contact region when interacting with an unknown environment. Nevertheless, utilizing touch information to plan exploration paths and adjust robot posture to improve task efficien ...

In Process Systems Engineering, computationally-demanding models are frequent and plentiful. Handling such complexity in an optimization framework in a fast and reliable way is essential, not only for generating meaningful solutions but also for providing ...

This work studies the power density (PD) optimization in wind farms, and its sensitivity to the available area size. A novel genetic algorithm (PDGA) is introduced, which optimizes PD and the turbine layout, by self-adapting to the PD and to the solutions ...

Design exploration is a creative process that consists of the incremental generation of design candidates. Supported by digital means or not, the process handles the ill-structured nature of design and allows creativity to flourish through diversity of des ...

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 ...

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 ...

LES and wind tunnel studies have shown significant benefit when allowing turbines (T) in a wind farm to adopt different heights. This work presents two new genetic algorithms (GA) that perform wind farm layout optimization (WFLO) involving continuous and t ...

Omnidirectional video streaming is usually implemented based on the representations of tiles, where the tiles are obtained by splitting the video frame into several rectangular areas and each tile is converted into multiple representations with different r ...

Optimisation of building form for solar energy utilisation using constrained evolutionary algorithms

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Data-driven control in atomic force microscopy using a genetic algorithm

Mspo: a Machine Learning Hyperparameter Optimization Method for Enhanced Breast Cancer Image Classification

Improving the accuracy and interpretability of neural networks for wind power forecasting

Diffusion Moving-Average Adaptation Over Networks

Scalable constrained optimization

A ride time-oriented scheduling algorithm for dial-a-ride problems

Learning to Optimize with Convergence Guarantees Using Nonlinear System Theory

Learning to Remove Cuts in Integer Linear Programming

The effect of smooth parametrizations on nonconvex optimization landscapes

Genetic Algorithms for the Discovery of Homogeneous Catalysts

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

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Online active and dynamic object shape exploration with a multi-fingered robotic hand

A combined genetic algorithm and active learning approach to build and test surrogate models in Process Systems Engineering

Wind farm power density optimization according to the area size using a novel self-adaptive genetic algorithm

Policy-based Exploration of Equilibrium Representations (PEER): A topology grammar for generative conceptual structural design

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

Byzantine-Resilient Learning Beyond Gradients: Distributing Evolutionary Search

Wind farm layout and unconstrained hub height optimization using genetic algorithms applied to different power densities

Quality-Constrained Encoding Optimization for Omnidirectional Video Streaming