A study of the neutrality of Boolean function landscapes in genetic programming

DOI10.1016/J.TCS.2011.03.011zbMath1237.68196DBLPjournals/tcs/VanneschiPMTCV12OpenAlexW2020055134WikidataQ57518457 ScholiaQ57518457MaRDI QIDQ418025

Sébastien Verel, Leonardo Vanneschi, Philippe Collard, Marco Tomassini, Giancarlo Mauri, Yuri Pirola

Publication date: 14 May 2012

Published in: Theoretical Computer Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.tcs.2011.03.011

zbMATH Keywords

Boolean functionsgenetic programmingneutralityproblem difficultyfitness landscapesnegative slope coefficient

Mathematics Subject Classification ID

Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20)

Related Items (5)

Multi-objectivization, fitness landscape transformation and search performance: a case of study on the HP model for protein structure prediction ⋮ Constraint-handling through multi-objective optimization: the hydrophobic-polar model for protein structure prediction ⋮ Evolving graphs with semantic neutral drift ⋮ On the effect of solution representation and neighborhood definition in AutoML fitness landscapes ⋮ Fitness landscape analysis of automated machine learning search spaces

Cites Work

• Neutrality in fitness landscapes.
• Lectures on Monte Carlo methods
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