Artificial Intelligence Applications in Engineering (MUH-920), week 5 of 14: interactive lab
Prof. Dr. Utku Kose, Süleyman Demirel University
Part A runs a genetic algorithm and differential evolution side by side on selectable test landscapes and on the constrained beam problem, with controls for population size, mutation, crossover and selection pressure, and repeats each run over several seeds [1, 2]. Part B samples beam designs, marks the dominated ones and draws the Pareto front of mass and deflection; selecting a point on the front shows its dimensions [3].
Grey dots are dominated designs, orange dots form the non-dominated set. Click near an orange dot to inspect the design.
Six questions with instant feedback. Rate your confidence before checking each answer.
Answers are saved in this browser only. The export creates a Markdown learning log for your portfolio.
After the export, continue with the discipline challenge and the weekly task in the week overview.
[1] Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley.
[2] Storn, R., & Price, K. (1997). Differential evolution: A simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11(4), 341-359. https://doi.org/10.1023/A:1008202821328
[3] Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182-197. https://doi.org/10.1109/4235.996017
[4] Wolpert, D. H., & Macready, W. G. (1997). No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1(1), 67-82. https://doi.org/10.1109/4235.585893