| Genetic algorithms (GA) and clustering techniques are used to study and classify materials. An analysis of the convergence speed of GA is carried out using advanced probability theory and random walk concepts. The determination of the ground-state of multicomponent alloys and Ising models with long-range interactions is accomplished using a genetic algorithm. A new GA operator, the domain-flip, is introduced and its efficiency is compared to that of traditional GA operators, crossover and mutation. The domain-flip operator destroys phase-boundaries by flipping all bits of a given domain at the same time. This operator turns out to be crucial in extracting the system from low local minima. Therefore its presence is rather essential to speed up the GA convergence. A study of GA convergence in its last stages, where all chromosomes present in the population are assumed to consist of two well-ordered domains, is performed using random walk theory and probability theory. Exact expressions for the average time needed for at least one chromosome to find the ground-state are derived. Also, the probability for two chromosomes to undergo a successful crossover, meaning the result is the ground-state, are given. Finally, clustering techniques, which belong to the field of Data Mining, are applied to the classification of materials. An improved version of the widely-used clustering algorithm, K-means, is developed. A comparison of the two clustering techniques on a two-dimensional data set shows that the guide-point approach is more powerful than the K-means algorithm. The guide-point algorithm is used successfully to partition a materials data set. This clustering results in extracting useful information from the data set for which no a priori knowledge was assumed. |
