(BSc Hons) Combining Machine Learning Techniques with Multi-Objective evolutionary Algorithms to Solve Real World Engineering Problems

Generative models for architecture prose and schematics

Multi-objective optimization of hydrostatic transmission performance with NSGA-II

Metaheuristic optimization framework for TV advertisement scheduling using NSGA-II, Genetic Algorithm, Simulated Annealing, and Tabu Search. Includes custom solution representation, constraint handling, initial population heuristics, and multi-objective evaluation (profit vs. cost).

Genetic algorithm tryna evolve a NN trading bot 🌚

A multi-objective optimization project using NSGA2

Graph Convolution Network GCN with Dimensional Redaction and Differential Algorithms using Python

Multi-Objective Evolutionary Algorithms with Wasserstein

This is my academic project about implementing an epidemic model using optimization methods.

Software desenvolvido como projeto de TCC sobre dimensionamento de Trocadores de Calor Casco e Tubo com o uso de GA's.

COBEM 2025 Paper for the implementation of Signed Log-Uniform Distribution (SLUD) versus Log-Uniform Distribution (LUD) and Linear Distribution in the initialization of variables in a Particle Swarm Optimization (PSO) optimization of a couple of benchmark functions.

(Completed) Machine Learning and Multi-Objective Evolutionary Algorithms to Solve Real World Engineering Problems (MultiObjectiveOptimisation and ML)

This project implements a multi-objective optimization model using evolutionary algorithms to schedule maintenance of power generation units over multiple time intervals. The goal is to maximize system reserve margins while minimizing total maintenance costs, subject to operational and budgetary constraints.

This repository is an implementation of https://link.springer.com/chapter/10.1007/978-3-030-72699-7_35 article. it uses evolutionary strategy (NSGA-II algorithm specificially) to configure image filters parameters in order to attack adversarially to a neural network.

A wrapper-based framework for pymoo problem modification.

MOObyMOEA: Multi-Objective Optimization using Multi-Objective Evolutionary Algorithms

A system written for my BSc Software Engineering dissertation, which optimises and visualises D&D characters to meet non-technical archetypes, using NSGA-II and 20 generations.

single & multi objective optimiztion

Multi objective optimization challenge, provided by the ESA & Topic of my thesis.

minimize the risk and to maximize the return in multi objective portfolio optimization