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HOW IT WORKS
DesignBuilder uses a Genetic Algorithm (GA) based on the NSGA-II method, which is widely used as a "fast and elitist multi-objective" method providing a good trade off between a well converged and a well distributed solution set. It works as follows:
| 1. | First, the population is randomly initialised. |
| 2. | Chromosomes (design variants) are sorted and put into fronts based on Pareto non-dominated sets. Within a Pareto front, the chromosomes are ranked based on Euclidean distances between solutions or I-dist (term used in NSGA-II) . Generally, solutions which are far away (not crowded) from other solutions are given a higher preference in the selection process to help create a diverse solution set and avoid crowding. |
| 3. | The best designs are picked from the current population and put into a mating pool. |
| 4. | In the mating pool, tournament selection, crossover and mating is carried out. |
| 5. | The mating pool and current population is combined. The resulting set is sorted, and the best chromosomes are passed into the new population. |
| 6. | Go to step 2, unless maximum number of generations have been reached. |
| 7. | The solution set is the highest ranked Pareto non-dominated set from all populations. |
BIBLIOGRAPHY
| 1. | Introduction to Genetic Algorithms: |
| 3. | Sean Luke, 2009, Essentials of Metaheuristics, Lulu, available for free at http://cs.gmu.edu/~sean/book/metaheuristics/ |
| 5. | Deb, K., Pratap. A, Agarwal, S., and Meyarivan, T. (2002). A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Transaction on Evolutionary Computation, 6(2), 181-197. |
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