A project on improving Neural Networks performance by using Genetic Algorithms.

This project proposes an efficient memetic algorithm for the graph coloring problem. Authors : L. Moalic (laurent.moalic@uha.fr) and A. Gondran (alexandre.gondran@enac.fr). Details are presented in the paper "Variations on memetic algorithms for graph coloring problems" :

The Stochastic Optimisation Software (SOS) is a research-oriented software platform for Metaheuristic Optimisation (Stochastic Optimisation). If you are using SOS, please acknowledge the article "Caraffini, F.; Iacca, G. The SOS Platform: Designing, Tuning and Statistically Benchmarking Optimisation Algorithms. Mathematics 2020, 8, 785." (https:…

A Bionomic Algorithm for the Aircraft Landing Problem

Implementation of Genetic Algorithm, Memetic Algorithm and Constraint Satisfaction on a Time Table scheduling problem. Also has an implementation of MiniMax Strategy for TicTacToe

A-List of all the Assignment done in Artificial Intelligence Course @IIIT-D

Implementation of metaheuristic optimization methods in Python for scientific, industrial, and educational scenarios. Experiments can be executed in parallel or in a distributed fashion. Experimental results can be evaluated in various ways, including diagrams, tables, and export to Excel.

Unicost Set Covering Problem

Convolutional Genetic Programming method for image classification

🌱 Genetic Algorithm, Memetic Algorithms, GRASP, Simulated Annealing, Multi start search, Reiterated Local Search, Local Search, Greedy and randomized Greedy

Shuffled Frog Leaping Algorithm (SFLA) in MATLAB

This project focuses on Adaptive University Timetabling Optimization using a hybrid approach combining Genetic Algorithm (GA) and genetic algorithm variation like memetic and cultural simulated annealing algorithm artificial bee clony ABC algorithm Particle Swarm Optimization (PSO).

MATLAB-based heuristic algorithm for GrVRP-PCAFS with segmentation and local search.

All the programming assignments done as a part of the AI course in IIIT Delhi

Shuffled Complex Evolution (SCE-UA) in MATLAB

The fastest implementation of "A powerful route minimization heuristic for the vehicle routing problem with time windows" Yuichi Nagata, Olli Bräysy

This is an implementation for solving the TSP problem using a genetic and a memetic algorithm. PySpark is used to use a distributed workload.

Implementation about a memetic algorithm, including a genetic algorithm and local search for our defined Minesweeper game.

Metaheuristics for finding good Low Autocorrelation Binary Sequences.

Second Year project (Module: Artificial Intelligence Methods), focusing on implementing and evaluating AI Optimisation methods.