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In the experiments, the computational efficiency of these non-dominated sorting methods is considered on random populations and MOEAs.
It is worth noting that deductive sort also achieves a competitive efficiency on random populations in terms of runtime despite that it uses more objective comparisons.
Second, ENS-SS performs better than T-ENS on random populations with four objectives in terms of runtime, despite that it consumes more objective comparisons than T-ENS.
Second, in terms of runtime, ENS-SS always achieves the best efficiency on random populations for two-objective and three-objective optimization.
First, in terms of number of objective comparisons, the generalized Jensen's sort performs the best on random populations for both two-objective and three-objective optimization.
First, the efficiency of T-ENS isuperioroverver the other seven non-dominated sorting methods in terms of both number of objective comparisons and runtime on random populations for many-objective optimization.
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(Since 1960, fourteen N.F.L. players have had a diagnosis of amyotrophic lateral sclerosis, which is about twelve more than you would expect from a random population sample).
Initially, a random population of possible solutions is created.
Initially, a random population is created in pop. 2.
Afterwards, we generate an initial random population of chromosomes.
Initially, the algorithm creates a random population of parameters.
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randomised populations
adventitious populations
uncertain populations
casual populations
arbitrary populations
sampling populations
isolated populations
random tweets
random sounds
random jobs
random collections
random friends
random killers
random pages
random acts
random birds
random snogs
random blackouts
random people
random thoughts
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