The No Free Lunch Theorem: What Are its Main Implications for the Optimization Practice?

DOI10.1007/978-3-030-66515-9_12OpenAlexW3167463485MaRDI QIDQ5153509

Loris Serafino

Publication date: 30 September 2021

Published in: Black Box Optimization, Machine Learning, and No-Free Lunch Theorems (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-030-66515-9_12

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Approximation methods and heuristics in mathematical programming (90C59)

Cites Work

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• Survey of modeling and optimization strategies to solve high-dimensional design problems with computationally-expensive black-box functions
• Knowledge incorporation in evolutionary computation
• A framework for optimization under limited information
• Optimizing Without Derivatives: What Does the No Free Lunch Theorem Actually Say?
• Introduction to Derivative-Free Optimization
• Practical Genetic Algorithms
• Metaheuristics—the metaphor exposed
• Global Optimization
• The elements of statistical learning. Data mining, inference, and prediction

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