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Beyond Genetic Algorithms - Understanding Simulation-based Optimization

INTRODUCTION The increasing prevalence of parametric design and performance simulations in computational design and research practices increases the relevance of simulation-based optimization. Simulation-based optimization not only offers a potential for automating the search for high-performing design candidates, but also a potential for better understanding design spaces, and thus design problems, in terms of quantitative performance. But a sometimesunreflective reliance on genetic algorithms to the exclusion of other, potentially more efficient and insightful, optimization methods has limited these potentials (Wortmann et al. 2017). This workshop introduces singleand multiobjective, simulation-based (i.e., black-box) optimization algorithms in Grasshopper in terms of their optimization strategies and performance, including but not limited to the Galapagos GA. The workshop introduces optimization algorithms from threemajor categories: metaheuristics, direct search, andmodelbased methods. BACKGROUND Metaheuristics include popular populational stochastic algorithms such as genetic algorithms and particle swarm optimization, direct search tends to be iterative and more deterministic. Model-based methodsutilize (surrogate)models of aproblem’s underlying fitness landscape, which they continuously update during the optimization process (Wortmann and Nannicini 2017). In other words, model-based methods harness machine-learning relatedmethods to hasten optimization processes.