@inproceedings {pub3341,
	title = {Preference-guided Adaptation of Deformation Representations for Evolutionary Design Optimization},
	author = {Andreas Johannes Richter AND Stefan Menzel AND Mario Botsch},
	year = {2017},
	month = {June},
	abstract = {A dynamic industrial design optimization requires high-quality optimization algorithms as well as adaptive representations to find the global solution for a given problem. For adapting the representation to changing environments or to new input we utilize the concept of evolvability, which in our interpretation consists of three criteria: variability, regularity, and improvement potential, where regularity and improvement potential characterize conflicting goals between exploration and exploitation. Our goal is the efficient adaptation of the representation according to a given preference weight between regularity and improvement potential. We propose a combination of two heuristics, Lloyd sampling and orthogonal least squares sampling, to initialize the adaptation process for a given preference weight. We show that this initialization improves the convergence speed of the adaptation process as well as the resulting fitness. We then realize a stepwise design optimization procedure by alternating the adaptation of the representation with optimization of the design. During the design optimization process we extract information which we exploit in the next adaptation phase. We show that an intermediate preference weight, balancing between regularity and improvement potential, allows to exploit this information and is robust to erroneous initial information. Thereby, we increase
the performance of the whole design optimization process.},
	publisher = {IEEE},
	booktitle = {IEEE Congress on Evolutionary Computation},
	city = {San Sebastian},
	pages = {2110-2119}
}