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Mathematical optimization / Applied mathematics / Computational complexity theory / Big O notation / Pseudo-Boolean function / Poisson distribution / Expected value / Linear programming relaxation / Pareto distribution / Mathematical analysis / Mathematics / Operations research


Single- and Multi-Objective Genetic Programming: New Bounds for Weighted ORDER and MAJORITY Anh Nguyen Tommaso Urli
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Document Date: 2014-09-12 00:58:22

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File Size: 2,45 MB

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The University of Adelaide Adelaide SA / AMD / /

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Australia / /

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pence / USD / /

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Computer Science The University of Adelaide Adelaide / /

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lowest complexity solution / worst possible initial solution / possible solution / genetic programming algorithms / arbitrary initial solution / given solution / population-based multiobjective genetic programming algorithm / multi-objective algorithms / evolutionary algorithms / mutation operator / search space / empty solution / non-redundant solution / /

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Debian GNU/Linux / /

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MAJORITY Anh Nguyen Tommaso Urli Markus Wagner Evolutionary Computation Group School / Italy Evolutionary Computation Group School / University of Adelaide Adelaide / /

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MO-WORDER / Anh Nguyen Tommaso Urli Markus / /

Position

conductor / representative / General / stochastic hill-climber / /

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Java / C / /

Technology

multi-objective GP algorithms / two genetic programming algorithms / Java / fitness The algorithm / Linux / Optimization algorithms / GP algorithm / multi-objective algorithms / 4.2 Algorithms / investigated algorithms / multi-objective SMO-GP algorithm / population-based multiobjective genetic programming algorithm / studied GP algorithms / GP The algorithm / /

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