An Introduction to Genetic Algorithms
An Introduction to Genetic Algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Theoretical Computer Science - Natural computing
Black box search: framework and methods
Black box search: framework and methods
Practical Genetic Algorithms with CD-ROM
Practical Genetic Algorithms with CD-ROM
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Discrete laplace operators: no free lunch
SGP '07 Proceedings of the fifth Eurographics symposium on Geometry processing
Focused no free lunch theorems
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Evolutionary Computation
Optimization, block designs and No Free Lunch theorems
Information Processing Letters
Continuous Lunches Are Free Plus the Design of Optimal Optimization Algorithms
Algorithmica - Including a Special Section on Genetic and Evolutionary Computation; Guest Editors: Benjamin Doerr, Frank Neumann and Ingo Wegener
Two broad classes of functions for which a no free lunch result does not hold
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
No free lunch and free leftovers theorems for multiobjective optimisation problems
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
No free lunch, program induction and combinatorial problems
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
Unbiased black box search algorithms
Proceedings of the 13th annual conference on Genetic and evolutionary computation
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
An improved boosting based on feature selection for corporate bankruptcy prediction
Expert Systems with Applications: An International Journal
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We extend previous results concerning black box search algorithms, presenting new theoretical tools related to no free lunch NFL where functions are restricted to some benchmark that need not be permutation closed, algorithms are restricted to some collection that need not be permutation closed or limited to some number of steps, or the performance measure is given. Minimax distinctions are considered from a geometric perspective, and basic results on performance matching are also presented.