Integrated framework for devising optimum generation schedules

Proceedings of the IEEE Conference on Evolutionary Computation11-40016

SimBa-2: Improving a Novel Similarity-Based Crossover for the Evolution of Artificial Neural Networks

This work presents SimBa-2, an improved version of a novel crossover specifically adapted to the evolutionary optimization of neural network designs that aims at overcoming one of the major problems of recombination, known as the permutation problem. The crossover is based on a so-called ‘local similarity’ between two individuals selected for the recombination process from the population, and it is applied according to a similarity threshold. An approach exploiting this operator has been implemented and applied to five benchmark classification problems in machine learning, chosen among some of the well known classification problems provided by the UCI Machine Learning Repository. The application of different similarity thresholds values has been investigated and the experimental results show how the behavior of the operator changes with respect to these values

A Lexicographic Encoding for Word Sense Disambiguation with Evolutionary Neural Networks

We propose a supervised approach to word sense disambiguation based on neural networks combined with evolutionary algorithms. Large tagged datasets for every sense of a polysemous word are considered, and used to evolve an optimized neural network that correctly disambiguates the sense of the given word considering the context in which it occurs. A new distributed scheme based on a lexicographic encoding to represent the context in which a particular word occurs is proposed. The viability of the approach has been demonstrated through experiments carried out on a representative set of polysemous words