Authors:
Gideon Avigad
;
Erella Eisenstadt
and
Uri Ben Hanan
Affiliation:
Ort Braude College of Engineering, Israel
Keyword(s):
Multiobjective, Evolution, Engineering design.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Evolutionary Computing
;
Evolutionary Multiobjective Optimization
;
Society and Cultural Aspects of Evolution
;
Soft Computing
Abstract:
In the same manner that species are associated with variants in order to survive, and that human communities, apparently in order to survive, are built up from people with different skills and professions, we suggest in this paper to select a set of diverse solutions in order to optimally solve Multi-Objective Problems (MOPs). As a set, the solutions may cover a wider range of capabilities within the multi-objective space than is possible for an individual member of the set. The diversity within the set is a key issue of this paper and hereinafter designated as an assortment. In the paper, we suggest a computational tool that supports the selection of such an assortment. The selection is posed as an auxiliary MOP of cost versus variability. The cost is directly related to the size of the assortment, whereas the variability is related to the ability of the assortment to cover the objective space. A previously treated problem is adopted and utilized in order to explain and demonstrate
the approach.
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