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

Code for the paper: Intrusion Detection in Networks by Wasserstein Enabled Many-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.

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

A multi-objective optimization project using NSGA2

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

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

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

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

A wrapper-based framework for pymoo problem modification.

single & multi objective optimiztion

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.

Generative models for architecture prose and schematics

pySWATPlus is a Python library tailored for seamless interaction with Soil and Water Assessment Tool Plus (SWAT+). Empowering users to efficiently manage input and output files within Python environments, pySWATPlus streamlines data manipulation and calibration processes using pymoo..

A Python implementation of the Knowledge Guided Bayesian Dynamic Multi-Objective Evolutionary Algorithm (KGB-DMOEA)

A Python wrapper for executing and calibrating the Soil and Water Assessment Tool (SWAT) in Unix/macOS systems.

Ship Routing Algorithms for Just-In-Time and Energy Efficient Voyages. By using a genetic algorithm we strive the lowest possible fuel consumption while at the same time keeping the scheduled deadlines. Two different specifications of the algorithm are available, one with a constant engine power, one with an over the route changeable engine power.

This repo demonstrates how to build a surrogate (proxy) model by multivariate regressing building energy consumption data (univariate and multivariate) and use (1) Bayesian framework, (2) Pyomo package, (3) Genetic algorithm with local search, and (4) Pymoo package to find optimum design parameters and minimum energy consumption.