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Concept

Bees algorithm

Publications associées (5)

Convergence without Convexity: Sampling, Optimization, and Games

Ya-Ping Hsieh

Many important problems in contemporary machine learning involve solving highly non- convex problems in sampling, optimization, or games. The absence of convexity poses significant challenges to convergence analysis of most training algorithms, and in some ...

EPFL2020

Heuristic algorithms for maximising the total profit of end-of-life computer remanufacturing

Dimitrios Kyritsis, Sangje Cho

Recently the optimisation of end-of-life (EOL) computer remanufacturing has been highlighted since a big amount of used computers have been disposed of every year. Each part inspected after disassembling EOL computers can have various EOL options such as r ...

Taylor & Francis Ltd2017

Model and metaheuristics for a scheduling problem integrating procurement, sale and distribution decisions

Rémy Glardon, Nicolas Zufferey

This paper presents an integrated approach for short-term supply chain management (SCM) at a fast moving consumer goods production plant. The problem is to determine the production quantities, to provide a detailed production schedule, to trigger the relev ...

Springer2017

Artificial Evolution by Viability Rather Than Competition

Dario Floreano, Andrea Maesani, Pradeep Ruben Fernando

Evolutionary algorithms are widespread heuristic methods inspired by natural evolution to solve difficult problems for which analytical approaches are not suitable. In many domains experimenters are not only interested in discovering optimal solutions, but ...

Public Library of Science2014

Exponential Natural Evolution Strategies

Daniël Pieter Wierstra

The family of natural evolution strategies (NES) offers a principled approach to real-valued evolutionary optimization by following the natural gradient of the expected fitness. Like the well-known CMA-ES, the most competitive algorithm in the field, NES c ...

2010