Gender approaches to evolutionary multi-objective optimization using pre-selection of criteria

A novel idea to perform evolutionary computations (ECs) for solving highly dimensional multi-objective optimization (MOO) problems is proposed. Following the general idea of evolution, it is proposed that information about gender is used to distinguish between various groups of objectives and identi...

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Bibliographic Details
Published in:Engineering optimization 2018-01, Vol.50 (1), p.120-144
Main Authors: Kowalczuk, Zdzisław, Białaszewski, Tomasz
Format: Article
Language:English
Subjects:
Criteria
engineering applications
Evolutionary algorithms
evolutionary learning
Fitness
Gender
Genetic algorithms
Intelligibility
multi-objectives
Multiple objective analysis
Optimization
Pareto optimality
Philosophy
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Summary:A novel idea to perform evolutionary computations (ECs) for solving highly dimensional multi-objective optimization (MOO) problems is proposed. Following the general idea of evolution, it is proposed that information about gender is used to distinguish between various groups of objectives and identify the (aggregate) nature of optimality of individuals (solutions). This identification is drawn out of the fitness of individuals and applied during parental crossover in the processes of evolutionary multi-objective optimization (EMOO). The article introduces the principles of the genetic-gender approach (GGA) and virtual gender approach (VGA), which are not just evolutionary techniques, but constitute a completely new rule (philosophy) for use in solving MOO tasks. The proposed approaches are validated against principal representatives of the EMOO algorithms of the state of the art in solving benchmark problems in the light of recognized EC performance criteria. The research shows the superiority of the gender approach in terms of effectiveness, reliability, transparency, intelligibility and MOO problem simplification, resulting in the great usefulness and practicability of GGA and VGA. Moreover, an important feature of GGA and VGA is that they alleviate the 'curse' of dimensionality typical of many engineering designs.
ISSN:0305-215X
1029-0273
DOI:10.1080/0305215X.2017.1305374