Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Reliability-based multi-objective optimization using evolutionary algorithms
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Evolutionary optimization in uncertain environments-a survey
IEEE Transactions on Evolutionary Computation
An Investigation on Noisy Environments in Evolutionary Multiobjective Optimization
IEEE Transactions on Evolutionary Computation
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Experiment-based optimization using Evolutionary Algorithms (EAs) is a promising approach for real world problems in which construction of simulation models is difficult. When using EAs, three difficulties have to be considered. Currently, two difficulties, uncertainty of the evaluation value and limitation of the number of evaluations, are active research topics into EAs. However, the other difficulty, avoidance of extreme trial, has not entered into the spotlight. Extreme trials run the 'risk' of breakdown of the optimized object and its measurement instruments in experiment-based optimization. In this paper, we consider that the extreme trial means a large constraint violation of the problems, and install the concept of 'risky-constraint'. Then, to avoid risky-constraint violation, we propose a violation avoidance method and combine it with Multi-objective Evolutionary Algorithms (MOEAs). The effectiveness of the proposed method is confirmed through numerical experiments and real common-rail diesel engine experiments.