| Budget Amount *help |
¥17,160,000 (Direct Cost: ¥13,200,000、Indirect Cost: ¥3,960,000)
Fiscal Year 2023: ¥2,730,000 (Direct Cost: ¥2,100,000、Indirect Cost: ¥630,000)
Fiscal Year 2022: ¥3,770,000 (Direct Cost: ¥2,900,000、Indirect Cost: ¥870,000)
Fiscal Year 2021: ¥3,380,000 (Direct Cost: ¥2,600,000、Indirect Cost: ¥780,000)
Fiscal Year 2020: ¥3,380,000 (Direct Cost: ¥2,600,000、Indirect Cost: ¥780,000)
Fiscal Year 2019: ¥3,900,000 (Direct Cost: ¥3,000,000、Indirect Cost: ¥900,000)
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| Outline of Final Research Achievements |
To examine the question, “Can the optimization process be automated by delegating decision-making to optimization methods?”, we developed a framework for automated decision-making for items that problem designers face in dealing with simulation-based optimization and that are directly related to the optimization results. Specifically, we proposed an automatic simulation accuracy selection mechanism based on rank correlation coefficients, proposed an optimization method that guarantees worst-case performance, analyzed the behavior of evolution strategies using a surrogate function, considered termination conditions by analyzing the convergence rate of evolution strategies, automatically constructed design variables using deep generative models for constrained optimization, and developed an efficient and effective optimization method.
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