Search Results for author:
Found 51 papers, 14 papers with code
Community Battery Energy Storage Systems for Enhancing Distribution System Operation: A Multi-objective Optimization Approach
The results show significant improvements in voltage regulation and DER utilization, demonstrating the potential of C-BESS in enabling more reliable DN operation.
CryptOpt: Automatic Optimization of Straightline Code
Manual engineering of high-performance implementations typically consumes many resources and requires in-depth knowledge of the hardware.
Assessing Domain Gap for Continual Domain Adaptation in Object Detection
To ensure reliable object detection in autonomous systems, the detector must be able to adapt to changes in appearance caused by environmental factors such as time of day, weather, and seasons.
Socialz: Multi-Feature Social Fuzz Testing
Online social networks have become an integral aspect of our daily lives and play a crucial role in shaping our relationships with others.
ELEA -- Build your own Evolutionary Algorithm in your Browser
We provide an open source framework to experiment with evolutionary algorithms which we call "Experimenting and Learning toolkit for Evolutionary Algorithms (ELEA)".
CryptOpt: Verified Compilation with Randomized Program Search for Cryptographic Primitives (full version)
Most software domains rely on compilers to translate high-level code to multiple different machine languages, with performance not too much worse than what developers would have the patience to write directly in assembly language.
Fairness in generative modeling
We design general-purpose algorithms for addressing fairness issues and mode collapse in generative modeling.
Automatically Categorising GitHub Repositories by Application Domain
GitHub is the largest host of open source software on the Internet.
Is Surprisal in Issue Trackers Actionable?
In this study we will propose a new method for unusual event detection in software repositories using surprisal.
Multi-Agent Task Assignment in Vehicular Edge Computing: A Regret-Matching Learning-Based Approach
Because of the high speed of vehicles and the limited communication range of road side units (RSUs), the computation tasks of participating vehicles are to be dynamically migrated across multiple servers.
On the Fitness Landscapes of Interdependency Models in the Travelling Thief Problem
Since its inception in 2013, the Travelling Thief Problem (TTP) has been widely studied as an example of problems with multiple interconnected sub-problems.
Run-of-Mine Stockyard Recovery Scheduling and Optimisation for Multiple Reclaimers
Stockpiles are essential in the mining value chain, assisting in maximising value and production.
Novelty-Driven Binary Particle Swarm Optimisation for Truss Optimisation Problems
Topology optimisation of trusses can be formulated as a combinatorial and multi-modal problem in which locating distinct optimal designs allows practitioners to choose the best design based on their preferences.
Efficiently solving the thief orienteering problem with a max-min ant colony optimization approach
In the ThOP, a thief has a time limit to steal items that distributed in a given set of cities.
Generating Instances with Performance Differences for More Than Just Two Algorithms
In recent years, Evolutionary Algorithms (EAs) have frequently been adopted to evolve instances for optimization problems that pose difficulties for one algorithm while being rather easy for a competitor and vice versa.
Metaheuristics "In the Large"
We describe our vision and report on progress, showing how the adoption of common protocols for all metaheuristics can help liberate the potential of the field, easing the exploration of the design space of metaheuristics.
A weighted-sum method for solving the bi-objective traveling thief problem
Many real-world optimization problems have multiple interacting components.
GTOPX Space Mission Benchmarks
This contribution introduces the GTOPX space mission benchmark collection, which is an extension of GTOP database published by the European Space Agency (ESA).
Genetic Improvement @ ICSE 2020
Following Prof. Mark Harman of Facebook's keynote and formal presentations (which are recorded in the proceedings) there was a wide ranging discussion at the eighth international Genetic Improvement workshop, GI-2020 @ ICSE (held as part of the 42nd ACM/IEEE International Conference on Software Engineering on Friday 3rd July 2020).
Benchmarking in Optimization: Best Practice and Open Issues
This survey compiles ideas and recommendations from more than a dozen researchers with different backgrounds and from different institutes around the world.
Fitness Landscape Analysis of Dimensionally-Aware Genetic Programming Featuring Feynman Equations
Genetic programming is an often-used technique for symbolic regression: finding symbolic expressions that match data from an unknown function.
MATE: A Model-based Algorithm Tuning Engine
In contrast to most static (feature-independent) algorithm tuning engines such as irace and SPOT, our approach aims to derive the best parameter configuration of a given algorithm for a specific problem, exploiting the relationships between the algorithm parameters and the features of the problem.
The Dynamic Travelling Thief Problem: Benchmarks and Performance of Evolutionary Algorithms
Many real-world optimisation problems involve dynamic and stochastic components.
An Annotated Dataset of Stack Overflow Post Edits
To improve software engineering, software repositories have been mined for code snippets and bug fixes.
Optimising the Fit of Stack Overflow Code Snippets into Existing Code
Software developers often reuse code from online sources such as Stack Overflow within their projects.
Software Engineering
Ants can orienteer a thief in their robbery
The Thief Orienteering Problem (ThOP) is a multi-component problem that combines features of two classic combinatorial optimization problems: Orienteering Problem and Knapsack Problem.
Hybrid Neuro-Evolutionary Method for Predicting Wind Turbine Power Output
Reliable wind turbine power prediction is imperative to the planning, scheduling and control of wind energy farms for stable power production.
Optimisation of Large Wave Farms using a Multi-strategy Evolutionary Framework
The primary goal of this research is to maximise the total harnessed power of a large wave farm consisting of fully-submerged three-tether wave energy converters (WECs).
An Evolutionary Deep Learning Method for Short-term Wind Speed Prediction: A Case Study of the Lillgrund Offshore Wind Farm
Accurate short-term wind speed forecasting is essential for large-scale integration of wind power generation.
A Non-Dominated Sorting Based Customized Random-Key Genetic Algorithm for the Bi-Objective Traveling Thief Problem
In this paper, we propose a method to solve a bi-objective variant of the well-studied Traveling Thief Problem (TTP).
Design optimisation of a multi-mode wave energy converter
A wave energy converter (WEC) similar to the CETO system developed by Carnegie Clean Energy is considered for design optimisation.
Is perturbation an effective restart strategy?
Premature convergence can be detrimental to the performance of search methods, which is why many search algorithms include restart strategies to deal with it.
A Hybrid Cooperative Co-evolution Algorithm Framework for Optimising Power Take Off and Placements of Wave Energy Converters
Such arrays can be optimised for total energy output by adjusting both the relative positions of buoys in farms and also the power-take-off (PTO) parameters for each buoy.
Algorithm Selection for Image Quality Assessment
We compared 8 state-of-the-art algorithms for blind IQA and showed that an oracle, able to predict the best performing method for any given input image, yields a hybrid method that could outperform even the best single existing method by a large margin.
Adaptive Neuro-Surrogate-Based Optimisation Method for Wave Energy Converters Placement Optimisation
The installed amount of renewable energy has expanded massively in recent years.
A new insight into the Position Optimization of Wave Energy Converters by a Hybrid Local Search
We compare the proposed hybrid method with other state-of-the-art search methods in five different wave scenarios -- one simplified irregular wave model and four real wave climates.
A Hybrid Evolutionary Algorithm Framework for Optimising Power Take Off and Placements of Wave Energy Converters
In this research, we investigate the problem of maximising the energy delivered by farms of wave energy converters (WEC's).
On resampling vs. adjusting probabilistic graphical models in estimation of distribution algorithms
This step should be completed at each iteration of BOA, resulting in a very time-consuming process.
A characterisation of S-box fitness landscapes in cryptography
Substitution Boxes (S-boxes) are nonlinear objects often used in the design of cryptographic algorithms.
Evolutionary Diversity Optimization Using Multi-Objective Indicators
Evolutionary diversity optimization aims to compute a diverse set of solutions where all solutions meet a given quality criterion.
An Improved Generic Bet-and-Run Strategy for Speeding Up Stochastic Local Search
We propose using more advanced methods to discriminate between "good" and "bad" sample runs, with the goal of increasing the correlation of the chosen run with the a-posteriori best one.
Predicting Good Configurations for GitHub and Stack Overflow Topic Models
Software repositories contain large amounts of textual data, ranging from source code comments and issue descriptions to questions, answers, and comments on Stack Overflow.
On the Effectiveness of Simple Success-Based Parameter Selection Mechanisms for Two Classical Discrete Black-Box Optimization Benchmark Problems
Despite significant empirical and theoretically supported evidence that non-static parameter choices can be strongly beneficial in evolutionary computation, the question how to best adjust parameter values plays only a marginal role in contemporary research on discrete black-box optimization.
Discrepancy-based Evolutionary Diversity Optimization
Diversity plays a crucial role in evolutionary computation.
Evolutionary Computation plus Dynamic Programming for the Bi-Objective Travelling Thief Problem
This research proposes a novel indicator-based hybrid evolutionary approach that combines approximate and exact algorithms.
Exact Approaches for the Travelling Thief Problem
Many evolutionary and constructive heuristic approaches have been introduced in order to solve the Traveling Thief Problem (TTP).
A Generic Bet-and-run Strategy for Speeding Up Traveling Salesperson and Minimum Vertex Cover
A common strategy for improving optimization algorithms is to restart the algorithm when it is believed to be trapped in an inferior part of the search space.
A case study of algorithm selection for the traveling thief problem
Many real-world problems are composed of several interacting components.
Evolutionary computation for multicomponent problems: opportunities and future directions
Over the past 30 years many researchers in the field of evolutionary computation have put a lot of effort to introduce various approaches for solving hard problems.
Seeding the Initial Population of Multi-Objective Evolutionary Algorithms: A Computational Study
We investigate the effect of two seeding techniques for five algorithms on 48 optimization problems with 2, 3, 4, 6, and 8 objectives.
Evolving Pacing Strategies for Team Pursuit Track Cycling
Team pursuit track cycling is a bicycle racing sport held on velodromes and is part of the Summer Olympics.
