In the field of artificial intelligence, a genetic algorithm (GA) is a search heuristic that mimics the process of natural selection. This heuristic (also sometimes called a metaheuristic) is routinely used to generate useful solutions to optimization and search problems. Genetic algorithms belong to the larger class of evolutionary algorithms (EA), which generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover [Reference: Wikipedia].
This code implements the MATLAB Genetic Algorithm (GA) function for optimization of the benchmark 10-bar truss problem with continuous design variables. More details about this problem and a comparison between results of different optimization methods are available in the following papers:
1-Multi-class teaching–learning-based optimization for truss design with frequency constraints
2-Design of space trusses using modified teaching learning based optimization
HelpGA.mp4 explains how to use the code.
Inspired by: Truss Analysis