38,870 research outputs found
A Data-Driven Evolutionary Transfer Optimization for Expensive Problems in Dynamic Environments
Many real-world problems are usually computationally costly and the objective
functions evolve over time. Data-driven, a.k.a. surrogate-assisted,
evolutionary optimization has been recognized as an effective approach for
tackling expensive black-box optimization problems in a static environment
whereas it has rarely been studied under dynamic environments. This paper
proposes a simple but effective transfer learning framework to empower
data-driven evolutionary optimization to solve dynamic optimization problems.
Specifically, it applies a hierarchical multi-output Gaussian process to
capture the correlation between data collected from different time steps with a
linearly increased number of hyperparameters. Furthermore, an adaptive source
task selection along with a bespoke warm staring initialization mechanisms are
proposed to better leverage the knowledge extracted from previous optimization
exercises. By doing so, the data-driven evolutionary optimization can jump
start the optimization in the new environment with a strictly limited
computational budget. Experiments on synthetic benchmark test problems and a
real-world case study demonstrate the effectiveness of our proposed algorithm
against nine state-of-the-art peer algorithms
Load-independent characterization of trade-off fronts for operational amplifiers
Abstract—In emerging design methodologies for analog integrated circuits, the use of performance trade-off fronts, also known as Pareto fronts, is a keystone to overcome the limitations of the traditional top-down methodologies. However, most techniques reported so far to generate the front neglect the effect of the surrounding circuitry (such as the output load impedance) on the Pareto-front, thereby making it only valid for the context where the front was generated. This strongly limits its use in hierarchical analog synthesis because of the heavy dependence of key performances on the surrounding circuitry, but, more importantly, because this circuitry remains unknown until the synthesis process. We will address this problem by proposing a new technique to generate the trade-off fronts that is independent of the load that the circuit has to drive. This idea is exploited for a commonly used circuit, the operational amplifier, and experimental results show that this is a promising approach to solve the issue
A Hierachical Evolutionary Algorithm for Multiobjective Optimization in IMRT
Purpose: Current inverse planning methods for IMRT are limited because they
are not designed to explore the trade-offs between the competing objectives
between the tumor and normal tissues. Our goal was to develop an efficient
multiobjective optimization algorithm that was flexible enough to handle any
form of objective function and that resulted in a set of Pareto optimal plans.
Methods: We developed a hierarchical evolutionary multiobjective algorithm
designed to quickly generate a diverse Pareto optimal set of IMRT plans that
meet all clinical constraints and reflect the trade-offs in the plans. The top
level of the hierarchical algorithm is a multiobjective evolutionary algorithm
(MOEA). The genes of the individuals generated in the MOEA are the parameters
that define the penalty function minimized during an accelerated deterministic
IMRT optimization that represents the bottom level of the hierarchy. The MOEA
incorporates clinical criteria to restrict the search space through protocol
objectives and then uses Pareto optimality among the fitness objectives to
select individuals.
Results: Acceleration techniques implemented on both levels of the
hierarchical algorithm resulted in short, practical runtimes for optimizations.
The MOEA improvements were evaluated for example prostate cases with one target
and two OARs. The modified MOEA dominated 11.3% of plans using a standard
genetic algorithm package. By implementing domination advantage and protocol
objectives, small diverse populations of clinically acceptable plans that were
only dominated 0.2% by the Pareto front could be generated in a fraction of an
hour.
Conclusions: Our MOEA produces a diverse Pareto optimal set of plans that
meet all dosimetric protocol criteria in a feasible amount of time. It
optimizes not only beamlet intensities but also objective function parameters
on a patient-specific basis
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