The proposed tutorial on Genetic Algorithms is designed to provide the basic exposure in Genetic Algorithms to graduate students and practicing engineers at large. The lectures will be decomposed into three sessions, and the organization for this session will be as follows:

Session 1 (The single objective approach)

Evolutionary Computation and its historical perspective. The American and German schools of thought. Simple Genetic Algorithms and its major operators: Reproduction, Crossover, Mutation and their variants. Genetic Algorithms for obtaining a sequence Mathematical Construction of Genetic Operators. Schema Theorem of John Holland. Variants of Binary Encoded Genetic Algorithms: Micro Genetic Algorithm, Messy Genetic Algorithm, Greedy Genetic Algorithm etc. Hamming Cliffs. Real Coded Genetic Algorithms. Differential Evolution. Elements of Evolutionary Strategies. Uni-modal vs. Multi-modal problems in Genetic Algorithms.

• Legal notice:
  

  Copyright (c) Pielaszek Research, all rights reserved.
  The above materials, including auxiliary resources, are subject to Publisher's copyright and the Author(s) intellectual rights. Without limiting Author(s) rights under respective Copyright Transfer Agreement, no part of the above documents may be reproduced without the express written permission of Pielaszek Research, the Publisher. Express permission from the Author(s) is required to use the above materials for academic purposes, such as lectures or scientific presentations.
  In every case, proper references including Author(s) name(s) and URL of this webpage: https://science24.com/paper/12173 must be provided.

1. Genetic algorithms: Multi-objective methods (tutorial lecture part II)
2. Tailor-made material design: An evolutionary approach using multi-objective genetic algorithms
3. The genetic algorithm-neural net combination (tutorial lecture part III)
4. Genetic algorithm based search on the role of variables in the work hardening process of multiphase steels
5. Modeling of recrystallization in cold rolled copper using inverse cellular automata and genetic algorithms
6. Genetic algorithm based multi-objective optimization of an ironmaking rotary kiln