Multi-objective optimisation using evolutionary algorithms constitutes a powerful computational framework that addresses complex problems involving conflicting objectives.

This paper frames hardware-aware neural network pruning as a multi-objective optimization problem and introduces HAMP, a memetic Multi-Objective Evolutionary Algorithm (MOEA) that optimizes both ...

This article explores the use of genetic algorithms to optimize the operation and maintenance assets of an offshore wind farm. Three different methods are implemented in order to demonstrate the ...

Multi-Objective Genetic Algorithms determine the Pareto set by giving higher priority to dominant portfolios in the evolutionary optimization techniques of selection and reproduction.

In the fast-evolving field of electronic systems design, engineers are under increasing pressure to deliver innovative, high-performance products within ever ...

Optimization seeks to find the best. It could be to design a process that minimizes capital or maximizes material conversion, to choose operating conditions that maximize throughput or minimize waste, ...

In an era where autonomous systems demand pinpoint accuracy, navigation algorithms face a tough trade-off between precision and speed.

Machine learning algorithms are gaining popularity in the hydrologic sciences. These algorithms often require tuning hyperparameters to tailor their performance to a specific purpose. Often these ...

Multi-objective optimisation using evolutionary algorithms constitutes a powerful computational framework that addresses complex problems involving conflicting objectives. By emulating natural ...