Continuous lunches are free plus the design of optimal optimization algorithms

DOI10.1007/s00453-008-9244-5zbMath1206.90133DBLPjournals/algorithmica/AugerT10OpenAlexW2006565216WikidataQ56431139 ScholiaQ56431139MaRDI QIDQ848643

Anne Auger, Olivier Teytaud

Publication date: 4 March 2010

Published in: Algorithmica (Search for Journal in Brave)

Full work available at URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.186.6007

zbMATH Keywords

complexitydynamic programmingno-free-lunchKolmogorov's extension theoremexpensive optimizationbandit-based Monte-Carlo planning

Mathematics Subject Classification ID

Approximation methods and heuristics in mathematical programming (90C59) Combinatorial optimization (90C27) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20)

Related Items (8)

No Free Lunch Theorems: Limitations and Perspectives of Metaheuristics ⋮ Designing an Optimal Search Algorithm with Respect to Prior Information ⋮ The Bayesian Search Game ⋮ Evolutionary optimization: pitfalls and booby traps ⋮ Exposing the grey wolf, moth‐flame, whale, firefly, bat, and antlion algorithms: six misleading optimization techniques inspired by bestial metaphors ⋮ A framework for co-optimization algorithm performance and its application to worst-case optimization ⋮ No-free-lunch theorems in the continuum ⋮ On the use of fitness landscape features in meta-learning based algorithm selection for the quadratic assignment problem

Uses Software

• DFO
• EGO

Cites Work

• An informational approach to the global optimization of expensive-to-evaluate functions
• Efficient global optimization of expensive black-box functions
• Recent progress in unconstrained nonlinear optimization without derivatives
• A derivative based surrogate model for approximating and optimizing the output of an expensive computer simulation
• A no-free-lunch theorem for non-uniform distributions of target functions
• Stochastic process measurability conditions
• Optimization with randomized search heuristics -- the (A)NFL theorem, realistic scenarios, and difficult functions.
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