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

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

Nikolaos Geroliminis, Claudia Bongiovanni, Mor Kaspi

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 ...

Pergamon-Elsevier Science Ltd2024

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

Anastasiia Komarova

Organic solvents are ubiquitous in industrial and domestic applications from the production of pharmaceuticals to household consumer products. The negative impact of most traditional solvents, especially aprotic types, on the environment, health, and safet ...

EPFL2024

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

Fernando Porté Agel, Nicolas Otto Kirchner Bossi

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 ...

Pergamon-Elsevier Science Ltd2023

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

François Maréchal, Julia Granacher

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 ...

Pergamon-Elsevier Science Ltd2023

Byzantine-Resilient Learning Beyond Gradients: Distributing Evolutionary Search

Rachid Guerraoui, Andrei Kucharavy, Matteo Monti

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

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

Fernando Porté Agel, Nicolas Otto Kirchner Bossi

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 ...

2022

Exploration of static equilibrium representations; policies and genetic algorithms

Corentin Jean Dominique Fivet, Ioannis Mirtsopoulos

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 ...

CRC Press2022

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

Dario Floreano, Giovanni Iacca, Davide Zappetti, 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

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

Rachid Guerraoui, Georgios Damaskinos

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 ...

Springer2021

Population pharmacokinetic model selection assisted by machine learning

Jan Sickmann Hesthaven, Nadia Terranova

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 ...

SPRINGER/PLENUM PUBLISHERS2022

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

Cyril Picard

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 ...

EPFL2021

Pareto-like sequential sampling heuristic for global optimisation

Katrin Beyer, Mahmoud S. M. Shaqfa

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 ...

2021

An efficient quantum algorithm for the time evolution of parameterized circuits

Giuseppe Carleo, Filippo Vicentini, Stefano Barison

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 ...

VEREIN FORDERUNG OPEN ACCESS PUBLIZIERENS QUANTENWISSENSCHAF2021

Spatial areas of genotype probability: Predicting the spatial distribution of adaptive genetic variants under future climatic conditions

Stéphane Joost, Estelle Rochat, Oliver Michele Selmoni

In a context of rapid global change, one of the key components for the survival of species is their genetic evolutionary potential for adaptation. Many methods have been developed to identify genetic variants underpinning adaptation to climate, but few too ...

2021

Roundtable Panel Discussion at DAC 2019: Evolutionary Computing or Heuristic Forever?

Massimiliano Di Ventra, Giovanni De Micheli

As demands for computing have been continuously increasing, various solutions have been proposed. Some approaches deal with improving algorithms and software programs, mainly through the tuning of advanced heuristics and learning methods. Some other tackle ...

2020

The electric autonomous dial-a-ride problem

Claudia Bongiovanni

This thesis develops mathematical programming frameworks to operate electric autonomous vehicles in the context of ride-sharing services. The introduced problem is a novel variant of the Dial-a-Ride Problem (DARP), denoted by the electric Autonomous Dial-a ...

EPFL2020

Pipeline Synthesis and Optimization from Branched Feedback Dataflow Programs

Marco Mattavelli, Anatoly Prihozhy, Endri Bezati, Simone Casale Brunet

Large dataflow designs are a result of behavioral specification of modern complex digital systems and/or a result of unfolding and transforming looped and branched programs. Since deep-submicron silicon technology provides large amounts of available resour ...

SPRINGER2020

The population genetics of crypsis in vertebrates: recent insights from mice, hares, and lizards

Jeffrey David Jensen, Kristen Kay Irwin, Stefan Jean Laurent, Susanne Petra Pfeifer

By combining well-established population genetic theory with high-throughput sequencing data from natural populations, major strides have recently been made in understanding how, why, and when vertebrate populations evolve crypsis. Here, we focus on backgr ...

NATURE PUBLISHING GROUP2020

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

Negar Kiyavash

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 ...

2020

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

Bart Deplancke

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 ...

2020