An improved genetic algorithm based on a novel selection strategy for nonlinear programming problems

Genetic algorithm is a heuristic population-based search method that incorporates three primary operators: crossover, mutation and selection. Selection operator plays a crucial role in finding optimal solution for constrained optimization problems. In this paper, an improved genetic algorithm (IGA)...

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Veröffentlicht in:	Computers & chemical engineering Jg. 35; H. 4; S. 615 - 621
Hauptverfasser:	Tang, Ke-Zong, Sun, Ting-Kai, Yang, Jing-Yu
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Kidlington Elsevier Ltd 07.04.2011 Elsevier
Schlagworte:	Applied sciences Constraint-handling Decision theory. Utility theory Exact sciences and technology Genetic algorithms Mathematical programming Non-dominated solution Nonlinear programming problems Operational research and scientific management Operational research. Management science Optimization Nonlinear programming problems Non-dominated solution Optimization Constraint-handling Genetic algorithms Constraint handling Local search Pareto optimum Process selection Partial ordering Constraint satisfaction Non linear programming Search algorithm Constrained optimization Selection problem Value function Genetic algorithm Optimal solution Heuristic method Robustness Objective function Mathematical programming
ISSN:	0098-1354, 1873-4375
Online-Zugang:	Volltext
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