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Joines, J. and Houck, C. (1994) On the Use of Non-Stationary Penalty Functions to Solve Constrained Optimization Problems with Genetic Algorithms. Proceedings of the 1st IEEE Conference on Evolutionary Computation, Orlando, 27-29 June 1994, 579-584.

has been cited by the following article:

• TITLE: An Innovative Genetic Algorithms-Based Inexact Non-Linear Programming Problem Solving Method

  AUTHORS: Weihua Jin, Zhiying Hu, Christine Chan

  KEYWORDS: Genetic Algorithms, Inexact Non-Linear Programming (INLP), Economy of Scale, Numeric Optimization, Solid Waste Management

  JOURNAL NAME: Journal of Environmental Protection, Vol.8 No.3, March 14, 2017

  ABSTRACT: In this paper, an innovative Genetic Algorithms (GA)-based inexact non-linear programming (GAINLP) problem solving approach has been proposed for solving non-linear programming optimization problems with inexact information (inexact non-linear operation programming). GAINLP was developed based on a GA-based inexact quadratic solving method. The Genetic Algorithm Solver of the Global Optimization Toolbox (GASGOT) developed by MATLABTM was adopted as the implementation environment of this study. GAINLP was applied to a municipality solid waste management case. The results from different scenarios indicated that the proposed GA-based heuristic optimization approach was able to generate a solution for a complicated nonlinear problem, which also involved uncertainty.