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

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.

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

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

Convolutional Genetic Programming method for image classification

This project proposes an efficient memetic algorithm for the graph coloring problem. Authors : L. Moalic ([email protected]) and A. Gondran ([email protected]). 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:…

Time Series Segmentation Algorithms

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 Bionomic Algorithm for the Aircraft Landing Problem

Shuffled Complex Evolution (SCE-UA) in MATLAB

💼 Approach to the Quadratic Assignment Problem in C++ using memetic algorithms and tabu search.

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

Solving N-Queens problem by implementing Memetic Algorithm.

Memetic Algorithms with Explicit Diversity Management for the Linear Ordering Problem.

Shuffled Frog Leaping Algorithm (SFLA) in MATLAB

Unicost Set Covering Problem

🧠 A set of metaheuristics applied in solving the QAP problem, including local search, genetic algorithms, memetics and more.

A genetic algorithm to solve the traveling salesman problem

This work was aimed at finding methods to identify the most distant proteins and most diverse subsets of proteins from large protein databases in a scalable and efficient way using a dataset of protein embeddings from SwissProt, data mining techniques and metaheuristics.