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Found 8 papers, 1 papers with code
Mining Potentially Explanatory Patterns via Partial Solutions
Genetic Algorithms have established their capability for solving many complex optimization problems.
How VADER is your AI? Towards a definition of artificial intelligence systems appropriate for regulation
Our online, publicly-available VADER framework scores the coverage of premises that should underlie AI definitions for regulation, which aim to (i) reproduce principles observed in other successful technology regulations, and (ii) include all AI techniques and approaches while excluding non-AI works.
Detection of a facemask in real-time using deep learning methods: Prevention of Covid 19
By the end of April 2021, the world has recorded 144, 358, 956 confirmed cases of novel-coronavirus disease (Covid-19) including 3, 066, 113 deaths according to the world health organization (WHO).
From Simulations to Reality: Enhancing Multi-Robot Exploration for Urban Search and Rescue
In this study, we present a novel hybrid algorithm, combining Levy Flight (LF) and Particle Swarm Optimization (PSO) (LF-PSO), tailored for efficient multi-robot exploration in unknown environments with limited communication and no global positioning information.
Enhancing Genetic Improvement Mutations Using Large Language Models
We find that the number of patches passing unit tests is up to 75% higher with LLM-based edits than with standard Insert edits.
Roulette-Wheel Selection-Based PSO Algorithm for Solving the Vehicle Routing Problem with Time Windows
The well-known Vehicle Routing Problem with Time Windows (VRPTW) aims to reduce the cost of moving goods between several destinations while accommodating constraints like set time windows for certain locations and vehicle capacity.
Towards Explainable Metaheuristic: Mining Surrogate Fitness Models for Importance of Variables
Through mining the surrogate model we can build a picture of the learning process of a GA, and thus an explanation of the solution presented by the GA.
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.
