This work presents the application of the omni-aiNet algorithm - an immune-inspired algorithm originally developed to solve single and multiobjective optimization problems - to the reconstruction of phylogenetic trees...
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This work presents the application of the omni-aiNet algorithm - an immune-inspired algorithm originally developed to solve single and multiobjective optimization problems - to the reconstruction of phylogenetic trees. The main goal of this work is to automatically evolve a population of phylogenetic unrooted trees, possibly with distinct topologies, by minimizing at the same time the minimal evolution and the mean-squared error criteria. The obtained set of phylogenetic trees contains non-dominated individuals that form the Pareto front and that represent the trade-off of the two conflicting objectives. Given this set of phylogenetic trees, two multicriterion decision-making techniques were applied in order to try to select the best solution within the Pareto front.
When the food supply finishes, or when the larvae of blowflies complete their development and migrate prior to the total removal of the larval substrate, they disperse to find adequate places for pupation, a process k...
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When the food supply finishes, or when the larvae of blowflies complete their development and migrate prior to the total removal of the larval substrate, they disperse to find adequate places for pupation, a process known as post-feeding larval dispersal. Based on experimental data of the initial and final configuration of the dispersion, the reproduction of such spatio-temporal behavior is achieved here by means of the evolutionary search for cellular automata with a distinct transition rule associated with each cell, also known as a nonuniform cellular automata, and with two states per cell in the lattice. Two-dimensional regular lattices and multivalued states will be considered and a practical question is the necessity of discovering a proper set of transition rules. Given that the number of rules is related to the number of cells in the lattice, the search space is very large and an evolution strategy is then considered to optimize the parameters of the transition rules, with two transition rules per cell. As the parameters to be optimized admit a physical interpretation, the obtained computational model can be analyzed to raise some hypothetical explanation of the observed spatio-temporal behavior.
In this paper, we propose the use of an immune-inspired approach called opt-aiNet to generate a diverse set of high-performance candidates to compose an ensemble of neural network classifiers. Being a population-based...
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In this paper, we propose the use of an immune-inspired approach called opt-aiNet to generate a diverse set of high-performance candidates to compose an ensemble of neural network classifiers. Being a population-based search algorithm, the opt-aiNet is capable of maintaining diversity and finding many high-performance solutions simultaneously, which are known to be desired features when synthesizing an ensemble. Concerning the selection and combination phases, the most relevant selection and combination techniques already proposed in the literature have been considered. The main contribution of this paper is the indication that there is no pair of selection/combination technique that can be considered the best one, because the performance of the obtained ensemble varies significantly with the current composition of the pool of candidates already produced by the generation phase. Notwithstanding, this variability in performance is not restricted to the choice of opt-aiNet as the generative device. As a consequence, to overcome the performance of the best individual classifier, every possible pairs of selection and combination techniques should be tried. Only with such an exhaustive search (notice that the main computational burden is usually related to the generation phase), the performance of the ensemble was invariably superior to the performance of the best individual classifier on four benchmark classification problems.
Once the design of artificial neural networks (ANN) may require the optimization of numerical and structural parameters, bio-inspired algorithms have been successfully applied to accomplish this task, since they are p...
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Once the design of artificial neural networks (ANN) may require the optimization of numerical and structural parameters, bio-inspired algorithms have been successfully applied to accomplish this task, since they are population-based search strategies capable of dealing successfully with complex and large search spaces, avoiding local minima. In this paper, we propose the use of an artificial immune system for learning feedforward ANN's topologies. Besides the number of neurons in the hidden layer, the algorithm also optimizes the type of activation function for each node. The use of a Bayesian framework to infer the weights and weight decay terms as well as to perform model selection allows us to find neural models with high generalization capability and low complexity, once the Occam's razor principle is incorporated into the framework. We demonstrate the applicability of the proposal on seven classification problems and promising results were obtained.
This paper proposes a supervised version of a learning algorithm for a constructive neuro-immune network. The proposed methodology is developed by taking ideas from the immune system and learning vector quantization. ...
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This paper proposes a supervised version of a learning algorithm for a constructive neuro-immune network. The proposed methodology is developed by taking ideas from the immune system and learning vector quantization. The resulting classification algorithm is characterized by high-performance, similar to the ones produced by alternative methods in the literature, and parsimonious solutions, with a much smaller set of prototypes per class when compared with the other approaches. The number of prototypes is automatically defined by the convergence criterion. The algorithm requires a single user-defined parameter for training, associated with the convergence criterion, and the computational cost is sufficiently reduced to support applications involving large data sets.
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