Abstract
Two genetic quantum-based algorithms are proposed for large scale problem analysis, and are compared with elite GA and a hybrid algorithm of GA and neural network (NN) in terms of computational efficiency with equal or better performance. Genetic quantum algorithms (GQA) similar to genetic algorithms (GA) maintain a population of individuals but each individual is composed of probabilistic quantum bits for preserving diversity, i.e. each individual of length m is equivalent to 2/sup m/ states. Also instead of crossover or mutation, GQA use quantum gates (QG) to update individuals and to guide the evolutionary process. The role of NN is to replace the time consuming state of finite element analysis, but the neuro-approximation introduces error in fitness prediction as well. In comparison, statistical analysis reveals that GQA is not only relatively simpler, but it also decreases optimization time while finding better solutions. Furthermore, the usage of neural networks helps further reduce finite element evaluations without significantly compromising quality of solutions.
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