Genetic algorithms (GAs) are stochastic methods that are widely used in search and optimization. The breeding process is the main driving mechanism for GAs that leads the way to find the global optimum. And the initial phase of the breeding process starts with parent selection. The selection utilized in a GA is effective on the convergence speed of the algorithm. A GA can use different selection mechanisms for choosing parents from the population and in many applications the process generally depends on the fitness values of the individuals. Artificial neural networks (ANNs) are used to decide the appropriate parents by the new hybrid algorithm proposed in this study. And the use of neural networks aims to produce better offspring during the GA search. The neural network utilized in this algorithm tries to learn the structural patterns and correlations that enable two parents to produce high-fit offspring. In the breeding process, the first parent is selected based on the fitness value as usual. Then it is the neural network that decides the appropriate mate for the first parent chosen. Hence, the selection mechanism is not solely dependent on the fitness values in this study. The algorithm is tested with seven benchmark functions. It is observed from results of these tests that the new selection method leads genetic algorithm to converge faster.

Many researchers use mathematical-engineering methods in different domains of life, and medical research is no exception. One area for application of such methods is to assist people with different forms of disabilities. The methods described in the following text are oriented towards the analysis of disordered children's speech with the diagnosis of Specific Language Impairment (SLI), also named as Developmental Dysphasia (DD), and the analysis of the expressive speech. Both methods make use of Kohonen Self-Organizing Maps (KSOM) or Supervised Self-Organizing Maps (SSOM) for the analysis and the classification of features from utterances of healthy and ill children, or adult speakers for emotions analysis. The possibility of cluster visualisation is used for monitoring of disorder trends and therapy success. These experiments also demonstrate the ability of the KSOM or SSOM to classify emotions.

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