Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
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Since the pioneer work of Evolution Strategies, experiment-based optimization is one of the promising applications of evolutionary computation. Recent progress in automatic control and instrumentation provides a smart environment called Hardware In the Loop Simulation (HILS) for such application. However, since optimization through experiment has severe condition of limited evaluation time and fluctuation of observation, we have to develop methodologies that overcome these problems. This paper discusses application of Multi-Objective Evolutionary Algorithms (MOEAs) to experiment-based optimization of control parameters of dynamical systems. In such applications, we have to apply various parameter candidates spreading near the Pareto frontier to the system, and it causes fluctuation of the observed criteria due to the transient response by parameter switching. For reduction of loss time caused by such transient response in evaluation of criteria, we propose techniques called Evaluation Order Scheduling and Evaluation Time Scheduling. Numerical experiments using a formal test problem and experiment in a HILS environment for real internal-combustion engines have demonstrated the effectiveness of the proposed methods.