Viability Principles for Constrained Optimization Using a (1+1)-CMA-ES

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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 ...

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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 ...

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 ...

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 ...

Several techniques have been proposed for automatically searching for FPGA switch-blocks which typically show some tangible advantage over the well-known academic architectures. However, the resulting switch-blocks usually exhibit high levels of irregulari ...

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 ...

Design exploration consists in browsing the design space and through the incremental generation of diverse design candidates increases creativity and diversity. When integrated into the early stages of the structural design process, the task is expected to ...

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 ...

Determining appropriate utility specifications for discrete choice models is time-consuming and prone to errors. With the availability of larger and larger datasets, as the number of possible specifications exponentially grows with the number of variables ...

Cross-validation is commonly used to select the recommendation algorithms that will generalize best on yet unknown data. Yet, in many situations the available dataset used for cross-validation is scarce and the selected algorithm might not be the best suit ...

A fit-for-purpose structural and statistical model is the first major requirement in population pharmacometric model development. In this manuscript we discuss how this complex and computationally intensive task could benefit from supervised machine learni ...

Machine intelligence greatly impacts almost all domains of our societies. It is profoundly changing the field of mechanical engineering with new technical possibilities and processes. The education of future engineers also needs to adapt in terms of techni ...

Nonconvex minimax problems appear frequently in emerging machine learning applications, such as generative adversarial networks and adversarial learning. Simple algorithms such as the gradient descent ascent (GDA) are the common practice for solving these ...

In this paper, we propose a simple global optimisation algorithm inspired by Pareto’s principle. This algorithm samples most of its solutions within prominent search domains and is equipped with a self-adaptive mechanism to control the dynamic tightening o ...

The emergence of Big Data has enabled new research perspectives in the discrete choice community. While the techniques to estimate Machine Learning models on a massive amount of data are well established, these have not yet been fully explored for the esti ...

Genetic variation is the fuel of evolution, with standing genetic variation especially important for short-term evolution and local adaptation. To date, studies of spatiotemporal patterns of genetic variation in natural populations have been challenging, a ...

The article links two seemingly different fundamental theoretical concepts of autopoiesis and homeostasis and tries to apply them to the realm of socio-technical systems with the use of the Fractal Enterprise Model (FEM). Autopoiesis is the property of a s ...

We introduce a novel hybrid algorithm to simulate the real-time evolution of quantum systems using parameterized quantum circuits. The method, named "projected - Variational Quantum Dynamics" (p-VQD) realizes an iterative, global projection of the exact ti ...

Viability Principles for Constrained Optimization Using a (1+1)-CMA-ES

Chattez avec Graph Search

On the Trade-off between Flatness and Optimization in Distributed Learning

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

Development of Sustainable Carbohydrate-Based Solvents by Acetal Functionalisation of Biomass

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

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

Byzantine-Resilient Learning Beyond Gradients: Distributing Evolutionary Search

Regularity Matters: Designing Practical FPGA Switch-Blocks

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

Exploration of static equilibrium representations; policies and genetic algorithms

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

Assisted specification of discrete choice models

The Imitation Game: Algorithm Selection by Exploiting Black-Box Recommenders

Population pharmacokinetic model selection assisted by machine learning

Automated Design: A Journey Across Modelling, Optimization, and Education

Global Convergence and Variance Reduction for a Class of Nonconvex-Nonconcave Minimax Problems

Pareto-like sequential sampling heuristic for global optimisation

Estimation of discrete choice models with hybrid stochastic adaptive batch size algorithms

Genomic Analysis of European Drosophila melanogaster Populations Reveals Longitudinal Structure, Continent-Wide Selection, and Previously Unknown DNA Viruses

Using Enterprise Models to Explain and Discuss Autopoiesis and Homeostasis in Socio-technical Systems

An efficient quantum algorithm for the time evolution of parameterized circuits