Genetic operators for high-level knowledge representations
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Genetic algorithms and classifier systems: foundations and future directions
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Foundations of neural networks
Foundations of neural networks
Neurocomputing
Neural network architectures: an introduction
Neural network architectures: an introduction
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
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In this paper we present a new technique to simulate polymer blends that overcomes the shortcomings in polymer system modeling. This method has an inherent advantage in that the vast existing information on polymer systems forms a critical part in the design process. The stages in the design begin with selecting potential candidates for blending using Neural Networks. Generally the parent polymers of the blend need to have certain properties and if the blend is miscible then it will reflect the properties of the parents. Once this step is finished the entire problem is encoded into a genetic algorithm using various models as fitness functions. We select the lattice fluid model of Sanchez and Lacombe (J. Polym. Sci. Polym. Lett. Ed., vol. 15, p. 71, 1977), which allows for a compressible lattice. After reaching a steady-state with the genetic algorithm we transform the now stochastic problem that satisfies detailed balance and the condition of ergodicity to a Markov Chain of states. This is done by first creating a transition matrix, and then using it on the incidence vector obtained from the final populations of the genetic algorithm. The resulting vector is converted back into a population of individuals that can be searched to find the individuals with the best fitness values. A high degree of convergence not seen using the genetic algorithm alone is obtained. We check this method with known systems that are miscible and then use it to predict miscibility on several unknown systems.