From fitness landscapes evolution to automatic local search algorithm generation

DOI10.1111/itor.12906OpenAlexW3110226048MaRDI QIDQ6082174

Frédéric Saubion, Unnamed Author, Adrien Goëffon

Publication date: 29 November 2023

Published in: International Transactions in Operational Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/itor.12906

zbMATH Keywords

local searchdiscrete optimizationevolution strategiesfitness landscapesNK modelhill-climbing

Mathematics Subject Classification ID

Operations research, mathematical programming (90-XX)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Correlated and uncorrelated fitness landscapes and how to tell the difference
• The noising method: A new method for combinatorial optimization
• Variable neighborhood search
• Applications of evolutionary computing. EvoWorkshops 2003: EvoBIO, EvoCOP, EvoIASP, EvoMUSART, EvoROB, and EvoSTIM, Essex, UK, April 14--16, 2003. Proceedings
• Evolution strategies. A comprehensive introduction
• Reciprocal sign epistasis is a necessary condition for multi-peaked fitness landscapes
• SATenstein: automatically building local search SAT solvers from components
• ParamILS: An Automatic Algorithm Configuration Framework
• Guided Local Search
• Metaheuristics—the metaphor exposed
• Surrogate‐based methods for black‐box optimization
• Biased‐randomized iterated local search for a multiperiod vehicle routing problem with price discounts for delivery flexibility
• An ILS heuristic for the ship scheduling problem: application in the oil industry

This page was built for publication: From fitness landscapes evolution to automatic local search algorithm generation