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Improving Pareto Optimal Designs Using Genetic Algorithms
Author(s) -
Gero John S.,
Louis Sushil J.
Publication year - 1995
Publication title -
computer‐aided civil and infrastructure engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.773
H-Index - 82
eISSN - 1467-8667
pISSN - 1093-9687
DOI - 10.1111/j.1467-8667.1995.tb00286.x
Subject(s) - pareto optimal , pareto principle , set (abstract data type) , mathematical optimization , computer science , optimal design , genetic algorithm , algorithm , encode , multi objective optimization , mathematics , machine learning , biochemistry , chemistry , gene , programming language
Pareto optimal designs are the best designs that can be produced for a given problem formulation for a given set of criteria when the criteria are not combined in any way. If the goal is to improve the performance in those criteria, then it is possible to manipulate the problem formulation to achieve an improvement. The approach adopted is to encode the formulation in a genetic algorithm and to allow the formulation to evolve in the direction of improving Pareto optimal designs. A set of rules (in the form of a shape grammar), the execution of which produces a design, is encoded as the genes in a genetic algorithm. However, the rule set is allowed to evolve, not just the order of execution of rules. We present an example demonstrating both the approach and its utility in improving Pareto optimal designs.