207 research outputs found
Scalable genetic programming by gene-pool optimal mixing and input-space entropy-based building-block learning
The Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) is a recently introduced model-based EA that has been shown to be capable of outperforming state-of-the-art alternative EAs in terms of scalability when solving discrete optimization problems. One of the key aspects of GOMEA's success is a variation operator that is designed to extensively exploit linkage models by effectively combining partial solutions. Here, we bring the strengths of GOMEA to Genetic Programming (GP), introducing GP-GOMEA. Under the hypothesis of having little problem-specific knowledge, and in an effort to design easy-to-use EAs, GP-GOMEA requires no parameter specification. On a set of well-known benchmark problems we find that GP-GOMEA outperforms standard GP while being on par with more recently introduced, state-of-the-art EAs. We furthermore introduce Input-space Entropy-based Building-block Learning (IEBL), a novel approach to identifying and encapsulating relevant building blocks (subroutines) into new terminals and functions. On problems with an inherent degree of modularity, IEBL can contribute to compact solution representations, providing a large potential for knock-on effects in performance. On the difficult, but highly modular Even Parity problem, GP-GOMEA+IEBL obtains excellent scalability, solving the 14-bit instance in less than 1 hour
Shrink-Perturb Improves Architecture Mixing during Population Based Training for Neural Architecture Search
In this work, we show that simultaneously training and mixing neural networks
is a promising way to conduct Neural Architecture Search (NAS). For
hyperparameter optimization, reusing the partially trained weights allows for
efficient search, as was previously demonstrated by the Population Based
Training (PBT) algorithm. We propose PBT-NAS, an adaptation of PBT to NAS where
architectures are improved during training by replacing poorly-performing
networks in a population with the result of mixing well-performing ones and
inheriting the weights using the shrink-perturb technique. After PBT-NAS
terminates, the created networks can be directly used without retraining.
PBT-NAS is highly parallelizable and effective: on challenging tasks (image
generation and reinforcement learning) PBT-NAS achieves superior performance
compared to baselines (random search and mutation-based PBT).Comment: 10 pages, 7 figures. Accepted at ECAI 202
Multi-Objective Population Based Training
Population Based Training (PBT) is an efficient hyperparameter optimization
algorithm. PBT is a single-objective algorithm, but many real-world
hyperparameter optimization problems involve two or more conflicting
objectives. In this work, we therefore introduce a multi-objective version of
PBT, MO-PBT. Our experiments on diverse multi-objective hyperparameter
optimization problems (Precision/Recall, Accuracy/Fairness,
Accuracy/Adversarial Robustness) show that MO-PBT outperforms random search,
single-objective PBT, and the state-of-the-art multi-objective hyperparameter
optimization algorithm MO-ASHA
The Impact of Asynchrony on Parallel Model-Based EAs
In a parallel EA one can strictly adhere to the generational clock, and wait
for all evaluations in a generation to be done. However, this idle time limits
the throughput of the algorithm and wastes computational resources.
Alternatively, an EA can be made asynchronous parallel. However, EAs using
classic recombination and selection operators (GAs) are known to suffer from an
evaluation time bias, which also influences the performance of the approach.
Model-Based Evolutionary Algorithms (MBEAs) are more scalable than classic GAs
by virtue of capturing the structure of a problem in a model. If this model is
learned through linkage learning based on the population, the learned model may
also capture biases. Thus, if an asynchronous parallel MBEA is also affected by
an evaluation time bias, this could result in learned models to be less suited
to solving the problem, reducing performance. Therefore, in this work, we study
the impact and presence of evaluation time biases on MBEAs in an asynchronous
parallelization setting, and compare this to the biases in GAs. We find that a
modern MBEA, GOMEA, is unaffected by evaluation time biases, while the more
classical MBEA, ECGA, is affected, much like GAs are.Comment: 9 pages, 3 figures, 3 tables, submitted to GECCO 202
Less is More: A Call to Focus on Simpler Models in Genetic Programming for Interpretable Machine Learning
Interpretability can be critical for the safe and responsible use of machine
learning models in high-stakes applications. So far, evolutionary computation
(EC), in particular in the form of genetic programming (GP), represents a key
enabler for the discovery of interpretable machine learning (IML) models. In
this short paper, we argue that research in GP for IML needs to focus on
searching in the space of low-complexity models, by investigating new kinds of
search strategies and recombination methods. Moreover, based on our experience
of bringing research into clinical practice, we believe that research should
strive to design better ways of modeling and pursuing interpretability, for the
obtained solutions to ultimately be most useful
Observer variation-aware medical image segmentation by combining deep learning and surrogate-assisted genetic algorithms
There has recently been great progress in automatic segmentation of medical
images with deep learning algorithms. In most works observer variation is
acknowledged to be a problem as it makes training data heterogeneous but so far
no attempts have been made to explicitly capture this variation. Here, we
propose an approach capable of mimicking different styles of segmentation,
which potentially can improve quality and clinical acceptance of automatic
segmentation methods. In this work, instead of training one neural network on
all available data, we train several neural networks on subgroups of data
belonging to different segmentation variations separately. Because a priori it
may be unclear what styles of segmentation exist in the data and because
different styles do not necessarily map one-on-one to different observers, the
subgroups should be automatically determined. We achieve this by searching for
the best data partition with a genetic algorithm. Therefore, each network can
learn a specific style of segmentation from grouped training data. We provide
proof of principle results for open-sourced prostate segmentation MRI data with
simulated observer variations. Our approach provides an improvement of up to
23% (depending on simulated variations) in terms of Dice and surface Dice
coefficients compared to one network trained on all data.Comment: 11 pages, 5 figures, SPIE Medical Imaging Conference - 202
An End-to-end Deep Learning Approach for Landmark Detection and Matching in Medical Images
Anatomical landmark correspondences in medical images can provide additional
guidance information for the alignment of two images, which, in turn, is
crucial for many medical applications. However, manual landmark annotation is
labor-intensive. Therefore, we propose an end-to-end deep learning approach to
automatically detect landmark correspondences in pairs of two-dimensional (2D)
images. Our approach consists of a Siamese neural network, which is trained to
identify salient locations in images as landmarks and predict matching
probabilities for landmark pairs from two different images. We trained our
approach on 2D transverse slices from 168 lower abdominal Computed Tomography
(CT) scans. We tested the approach on 22,206 pairs of 2D slices with varying
levels of intensity, affine, and elastic transformations. The proposed approach
finds an average of 639, 466, and 370 landmark matches per image pair for
intensity, affine, and elastic transformations, respectively, with spatial
matching errors of at most 1 mm. Further, more than 99% of the landmark pairs
are within a spatial matching error of 2 mm, 4 mm, and 8 mm for image pairs
with intensity, affine, and elastic transformations, respectively. To
investigate the utility of our developed approach in a clinical setting, we
also tested our approach on pairs of transverse slices selected from follow-up
CT scans of three patients. Visual inspection of the results revealed landmark
matches in both bony anatomical regions as well as in soft tissues lacking
prominent intensity gradients.Comment: SPIE Medical Imaging Conference - 202
Bi-objective optimization of organ properties for the simulation of intracavitary brachytherapy applicator placement in cervical cancer
Validation of deformable image registration techniques is extremely
important, but hard, especially when complex deformations or content mismatch
are involved. These complex deformations and content mismatch, for example,
occur after the placement of an applicator for brachytherapy for cervical
cancer. Virtual phantoms could enable the creation of validation data sets with
ground truth deformations that simulate the large deformations that occur
between image acquisitions. However, the quality of the multi-organ Finite
Element Method (FEM)-based simulations is dependent on the patient-specific
external forces and mechanical properties assigned to the organs. A common
approach to calibrate these simulation parameters is through optimization,
finding the parameter settings that optimize the match between the outcome of
the simulation and reality. When considering inherently simplified organ
models, we hypothesize that the optimal deformations of one organ cannot be
achieved with a single parameter setting without compromising the optimality of
the deformation of the surrounding organs. This means that there will be a
trade-off between the optimal deformations of adjacent organs, such as the
vagina-uterus and bladder. This work therefore proposes and evaluates a
multi-objective optimization approach where the trade-off between organ
deformations can be assessed after optimization. We showcase what the extent of
the trade-off looks like when bi-objectively optimizing the patient-specific
mechanical properties and external forces of the vagina-uterus and bladder for
FEM-based simulations
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