Search Results for author:
Found 12 papers, 1 papers with code
Avoiding Redundant Restarts in Multimodal Global Optimization
Na\"ive restarts of global optimization solvers when operating on multimodal search landscapes may resemble the Coupon's Collector Problem, with a potential to waste significant function evaluations budget on revisiting the same basins of attractions.
Lessons Learned Report: Super-Resolution for Detection Tasks in Engineering Problem-Solving
We describe the lessons learned from targeting agricultural detection problem-solving, when subject to low resolution input maps, by means of Machine Learning-based super-resolution approaches.
Saliency Can Be All You Need In Contrastive Self-Supervised Learning
We propose an augmentation policy for Contrastive Self-Supervised Learning (SSL) in the form of an already established Salient Image Segmentation technique entitled Global Contrast based Salient Region Detection.
Toward an ImageNet Library of Functions for Global Optimization Benchmarking
Knowledge of search-landscape features of BlackBox Optimization (BBO) problems offers valuable information in light of the Algorithm Selection and/or Configuration problems.
The Unreasonable Effectiveness of the Final Batch Normalization Layer
Early-stage disease indications are rarely recorded in real-world domains, such as Agriculture and Healthcare, and yet, their accurate identification is critical in that point of time.
Addressing the Multiplicity of Solutions in Optical Lens Design as a Niching Evolutionary Algorithms Computational Challenge
Optimal Lens Design constitutes a fundamental, long-standing real-world optimization challenge.
Improving Model Accuracy for Imbalanced Image Classification Tasks by Adding a Final Batch Normalization Layer: An Empirical Study
We empirically observe that the initial F1 test score jumps from 0. 29 to 0. 95 for the minority class upon adding a final Batch Normalization (BN) layer just before the output layer in VGG19.
Multi-Level Evolution Strategies for High-Resolution Black-Box Control
Such problems arise in engineering and scientific applications, which possess a multi-resolution control nature, and thus may be formulated either by means of low-resolution variants (providing coarser approximations with presumably lower accuracy for the general problem) or by high-resolution controls.
Benchmarking Discrete Optimization Heuristics with IOHprofiler
Automated benchmarking environments aim to support researchers in understanding how different algorithms perform on different types of optimization problems.
On the Covariance-Hessian Relation in Evolution Strategies
We consider Evolution Strategies operating only with isotropic Gaussian mutations on positive quadratic objective functions, and investigate the covariance matrix when constructed out of selected individuals by truncation.
Protein design by multiobjective optimization: evolutionary and non-evolutionary approaches
We formulate a bi-objective combinatorial minimization problem that targets both stability and specificity of the 4-level heterotrimer.
On the Theoretical Capacity of Evolution Strategies to Statistically Learn the Landscape Hessian
We study the theoretical capacity to statistically learn local landscape information by Evolution Strategies (ESs).
