Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Some experiments in machine learning using vector evaluated genetic algorithms (artificial intelligence, optimization, adaptation, pattern recognition)
Multi-objective genetic algorithms: Problem difficulties and construction of test problems
Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Expert Systems with Applications: An International Journal
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In this paper, a real-world test problem is presented and made available for use by the EMO community. The problem deals with the optimization of polymer extrusion, in terms of setting the operating conditions and/or the screw geometry. The binary code of a computer program that predicts the thermomechanical experience of a polymer inside the machine, as a function of geometry, polymer properties and operating conditions, is developed. The program can be used through input and output data files, so that the parameters to optimize and the criteria evaluated data is communicated in both directions. Two distinct EMO algorithms are used to illustrate and test the optimization of this problem.