An explanation of ordinal optimization: Soft computing for hard problems
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Publication:1818951
DOI10.1016/S0020-0255(98)10056-7zbMath0931.90072WikidataQ126322766 ScholiaQ126322766MaRDI QIDQ1818951
Publication date: 5 January 2000
Published in: Information Sciences (Search for Journal in Brave)
Applications of mathematical programming (90C90) Special problems of linear programming (transportation, multi-index, data envelopment analysis, etc.) (90C08)
Related Items (15)
Constrained ordinal optimization -- a feasibility model based approach ⋮ Demonstration of probabilistic ordinal optimization concepts for continuous-variable optimization under uncertainty ⋮ Quantifying heuristics in the ordinal optimization framework ⋮ Simple explanation of the no-free-lunch theorem and its implications ⋮ Multi-objective ordinal optimization for simulation optimization problems ⋮ Comparison of selection rules for ordinal optimization ⋮ Embedding evolutionary strategy in ordinal optimization for hard optimization problems ⋮ The equivalence between ordinal optimization in deterministic complex problems and in stochastic simulation problems ⋮ A new approach to discrete stochastic optimization problems ⋮ Ordinal optimization and quantification of heuristic designs ⋮ Simulation optimization: a review of algorithms and applications ⋮ Constraint ordinal optimization ⋮ Ordinal optimization of \(G/G/1/K\) polling systems with \(k\)-limited service discipline ⋮ An ordinal optimization based evolution strategy to schedule complex make-to-order products ⋮ A probabilistic solution-generator for simulation
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- Ordinal optimization of DEDS
- Universal alignment probabilities and subset selection for ordinal optimization
- Convergence properties of ordinal comparison in the simulation of discrete event dynamic systems
- Rates of convergence of ordinal comparison for dependent discrete event dynamic systems
- Dynamics and convergence rate of ordinal comparison of stochastic discrete-event systems
- Explanation of goal softening in ordinal optimization
- Motion planning of walking robots in environments with uncertainty
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