This study proposes the modification of the neuroevolution of augmented topologies, namely the difference-based mutation operator. The difference-based mutation changes the weights of the neural network by combining the weights of several other networks at the position of the connections having same innovation numbers. The implemented neuroevolution algorithm allows backward connections and loops in the topology, and uses several mutation operators, including connections deletion. The algorithm is tested on a set of classification problems and a rotary inverted pendulum problem and compared to the same approach without difference-based mutation. The experimental results show that the proposed weight tuning scheme allows significant improvements of classification quality in several cases and finding better control algorithms.
Previously, a meta-heuristic approach called Co-Operation of Biology Related Algorithms, or COBRA for short, based on a fuzzy logic controller for solving real-valued optimization problems was introduced and described. The basic idea of the originally proposed approach consists in a cooperative work of six well-known biology-inspired algorithms (components) with similar schemes. Furthermore, the fuzzy logic controller determines which biology-inspired algorithms should be included in the co-operative work and their population sizes at a given moment for solving optimization problems using the COBRA approach. In this study a new modification of the COBRA approach based on an alternative way of generating potential solutions is proposed. The stated technique uses a historical memory of successful positions found by individuals to guide them in different directions and thus to improve their exploration and exploitation abilities. The proposed method was applied to the components of the COBRA approach and to its basic procedures. The modified meta-heuristic as well as other variants of the COBRA algorithm and components (with and without the proposed modification) were evaluated on three sets of low- and high-dimensional benchmark problems. The experimental results obtained by all algorithms are presented and compared. It was concluded that the fuzzy-controlled COBRA with success-history based position adaptation allows better solutions to be found than the other mentioned biology-inspired algorithms with the same computational effort. Thus, the usefulness of the proposed position adaptation technique was demonstrated.
In this study the confidence-based voting of neural net classifier and fuzzy logic based classifiers is proposed. In this method, for the cases when the fuzzy system is confident enough in its decision, i.e. when the membership value is large enough, fuzzy system makes the decision, otherwise, the neural net is applied. This allows classifying most of the objects by explainable interpretable fuzzy system, while using the more accurate neural network for the most difficult cases. The experiments are performed on a set of test datasets, and two problems of identifying the emotional state of a person using the data collected by non-contact vital Doppler sensors. The results show that this setup allows not only improving the classification quality, but also allows to explain the classification process the explanation of the classifier functioning.
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