A New Lagrangian Problem Crossover: A Systematic Review and
Meta-Analysis of Crossover Standerds
- URL: http://arxiv.org/abs/2204.10890v1
- Date: Thu, 21 Apr 2022 15:50:02 GMT
- Title: A New Lagrangian Problem Crossover: A Systematic Review and
Meta-Analysis of Crossover Standerds
- Authors: Aso M. Aladdin, Tarik A. Rashid
- Abstract summary: This paper presents an overview of crossover standards classification.
The significance of novel standard crossover is proposed depending on Lagrangian Dual Function (LDF)
The accuracy and performance of the proposed standard have evaluated by three unimodal test functions.
- Score: 1.1802674324027231
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The performance of most evolutionary metaheuristic algorithms depends on
various operators. The crossover operator is one of them and is mainly
classified into two standards; application-dependent crossover operators and
application-independent crossover operators. These standards always help to
choose the best-fitted point in the evolutionary algorithm process. The high
efficiency of crossover operators enables minimizing the error that occurred in
engineering application optimization within a short time and cost. There are
two crucial objectives behind this paper; at first, it is an overview of
crossover standards classification that has been used by researchers for
solving engineering operations and problem representation. The second objective
of this paper; The significance of novel standard crossover is proposed
depending on Lagrangian Dual Function (LDF) to progress the formulation of the
Lagrangian Problem Crossover (LPX) as a new systematic standard operator. The
results of the proposed crossover standards for 100 generations of parent
chromosomes are compared to the BX and SBX standards, which are the communal
real-coded crossover standards. The accuracy and performance of the proposed
standard have evaluated by three unimodal test functions. Besides, the proposed
standard results are statistically demonstrated and proved that it has an
excessive ability to generate and enhance the novel optimization algorithm
compared to BX and SBX.
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