Search Results for author:
Found 25 papers, 5 papers with code
Large-scale Benchmarking of Metaphor-based Optimization Heuristics
The number of proposed iterative optimization heuristics is growing steadily, and with this growth, there have been many points of discussion within the wider community.
Impact of spatial transformations on landscape features of CEC2022 basic benchmark problems
When benchmarking optimization heuristics, we need to take care to avoid an algorithm exploiting biases in the construction of the used problems.
Solving Deep Reinforcement Learning Benchmarks with Linear Policy Networks
Although Deep Reinforcement Learning (DRL) methods can learn effective policies for challenging problems such as Atari games and robotics tasks, algorithms are complex and training times are often long.
Explainable Benchmarking for Iterative Optimization Heuristics
Introducing the IOH-Xplainer software framework, for analyzing and understanding the performance of various optimization algorithms and the impact of their different components and hyper-parameters.
MA-VAE: Multi-head Attention-based Variational Autoencoder Approach for Anomaly Detection in Multivariate Time-series Applied to Automotive Endurance Powertrain Testing
A clear need for automatic anomaly detection applied to automotive testing has emerged as more and more attention is paid to the data recorded and manual evaluation by humans reaches its capacity.
Representation-agnostic distance-driven perturbation for optimizing ill-conditioned problems
Locality is a crucial property for efficiently optimising black-box problems with randomized search heuristics.
Analysis of modular CMA-ES on strict box-constrained problems in the SBOX-COST benchmarking suite
Specifically, we want to understand the performance difference between BBOB and SBOX-COST as a function of two initialization methods and six constraint-handling strategies all tested with modular CMA-ES.
Challenges of ELA-guided Function Evolution using Genetic Programming
Within the optimization community, the question of how to generate new optimization problems has been gaining traction in recent years.
Patterns of Convergence and Bound Constraint Violation in Differential Evolution on SBOX-COST Benchmarking Suite
This study investigates the influence of several bound constraint handling methods (BCHMs) on the search process specific to Differential Evolution (DE), with a focus on identifying similarities between BCHMs and grouping patterns with respect to the number of cases when a BCHM is activated.
When to be Discrete: Analyzing Algorithm Performance on Discretized Continuous Problems
In this work, we use the notion of the resolution of continuous variables to discretize problems from the continuous domain.
Modular Differential Evolution
Although these contributions are often compared to the base algorithm, it is challenging to make fair comparisons between larger sets of algorithm variants.
Deep-BIAS: Detecting Structural Bias using Explainable AI
Recently, the BIAS toolbox was introduced as a behaviour benchmark to detect structural bias (SB) in search algorithms.
Multi-surrogate Assisted Efficient Global Optimization for Discrete Problems
Decades of progress in simulation-based surrogate-assisted optimization and unprecedented growth in computational power have enabled researchers and practitioners to optimize previously intractable complex engineering problems.
BBOB Instance Analysis: Landscape Properties and Algorithm Performance across Problem Instances
Benchmarking is a key aspect of research into optimization algorithms, and as such the way in which the most popular benchmark suites are designed implicitly guides some parts of algorithm design.
Optimizing Stimulus Energy for Cochlear Implants with a Machine Learning Model of the Auditory Nerve
In the second part of this paper, the Convolutional Neural Network surrogate model was used by an Evolutionary Algorithm to optimize the shape of the stimulus waveform in terms energy efficiency.
The importance of being constrained: dealing with infeasible solutions in Differential Evolution and beyond
Here, we demonstrate that, at least in algorithms based on Differential Evolution, this choice induces notably different behaviours - in terms of performance, disruptiveness and population diversity.
Using Machine Learning to Detect Rotational Symmetries from Reflectional Symmetries in 2D Images
We demonstrate and analyze the performance of the extended algorithm to detect localised symmetries and the machine learning model to classify rotational symmetries.
Deep Multiagent Reinforcement Learning: Challenges and Directions
This paper surveys the field of deep multiagent reinforcement learning.
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.
Quantifying the Impact of Boundary Constraint Handling Methods on Differential Evolution
Constraint handling is one of the most influential aspects of applying metaheuristics to real-world applications, which can hamper the search progress if treated improperly.
Emergence of Structural Bias in Differential Evolution
Heuristic optimisation algorithms are in high demand due to the overwhelming amount of complex optimisation problems that need to be solved.
Is there Anisotropy in Structural Bias?
We find that anisotropy is very rare, and even in cases where it is present, there are clear tests for SB which do not rely on any assumptions of isotropy, so we can safely expand the suite of SB tests to encompass these kinds of deficiencies not found by the original tests.
Differential evolution outside the box
A wide range of popular Differential Evolution configurations is considered in this study.
Infeasibility and structural bias in Differential Evolution
This paper thoroughly investigates a range of popular DE configurations to identify components responsible for the emergence of structural bias - recently identified tendency of the algorithm to prefer some regions of the search space for reasons directly unrelated to the objective function values.
Structural bias in population-based algorithms
Theory predicts that structural bias is exacerbated with increasing population size and problem difficulty.
