Genetic Programming offers freedom in the definition of the cost function that is unparalleled among supervised learning algorithms. However, this freedom goes largely unexploited...
In genetic programming, there is a tendency for individuals in a population to accumulate fragments of code – often called introns – which are redundant in the fitness evaluat...
In this paper we show how genetic programming can be used to discover useful texture feature extraction algorithms. Grey level histograms of different textures are used as inputs ...
Abstract. Spector et al. have shown [1],[2],[3] that genetic programming can be used to evolve quantum circuits. In this paper, we present new results in this field, introducing p...
Analyzing the computational complexity of evolutionary algorithms (EAs) for binary search spaces has significantly informed our understanding of EAs in general. With this paper, ...