Covariance Matrix Adaptation for Multi-objective Optimization
Evolutionary Computation
The evolutionary algorithm SAMOA with use of design of experiments
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
The evolutionary algorithm SAMOA with use of design of experiments
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
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Common evolutionary algorithms are often inapplicable to technical design problems in the automotive industry. They cannot deal with multi-objective optimization problems, they need too many function evaluations and time and they cannot handle with a priori unknown dynamic constraints. They also fail because of the growing complexity of the optimization tasks and the huge experimental effort. Therefore, the principle target of this contribution is the improvement of the simulation and optimization techniques on technical design problems. For this task the new developed evolutionary algorithm SAMOA is presented, which is a combination of a genetic algorithm and an evolutionary strategy and can handle with a priori unknown dynamic constraints. It works with a number of potential solutions in progress and it is variable, robust and powerful. It can operate parallel and can deal with multi-criteria problems.