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The correlation between the objective reduction achieved, the dimension N and the geometric variance of the reduced-dimensionality space is shown and found significant.
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However, the existing research on objective reduction focuses on global redundancy between objectives [144, 145, 146], but little work has been conducted on locally conflicting objectives.
This study developed an evolutionary algorithm to overcome inefficiency of the objective constraint by introducing preference-ordering and successive objective reduction to the conventional multi-objective optimization module.
In order to solve the many-objective optimization model efficiently, an improved objective reduction (IOR) approach is proposed, aiming at acquiring the smallest set of objectives.
The Pareto corner search evolutionary algorithm (PCSEA) [145] is a newly proposed objective reduction approach.
The two teamed up, with Hameroff showing how synaptic inputs could 'orchestrate' Penrose objective reductions in neuronal microtubules, hence 'orchestrated objective reduction' (Orch OR).
There was no objective reduction in PSA.
There is a lack of fit between the objective theme and imprecise execution.
The key dimension highlighted in the present paper is the relationship between the objectives of a social protection mechanism (to protect, integrate and develop people) and the concrete result (reduction of social exclusion).
Where there is conflict between the objectives of achieving cost recovery and preserving access to justice, the latter objective must prevail".
The objective of total reduction was not achieved.
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