The learning in a niche based learning classifier system depends both on the complexity of the problem space and on the number of available actions. In this paper, we introduce a ...
A primary goal of evolutionary robotics is to create systems that are as robust and adaptive as the human body. Moving toward this goal often involves training control systems tha...
The Probabilistic Adaptive Mapping Developmental Genetic Programming (PAM DGP) algorithm that cooperatively coevolves a population of adaptive mappings and associated genotypes is...
Stock selection for hedge fund portfolios is a challenging problem for Genetic Programming (GP) because the markets (the environment in which the GP solution must survive) are dyn...
Identification of Transcription Factor Binding Site (TFBS) motifs in multiple DNA upstream sequences is important in understanding the mechanism of gene regulation. This identific...
The Negative Slope Coefficient (nsc) is an empirical measure of problem hardness based on the analysis of offspring-fitness vs. parent-fitness scatterplots. The nsc has been teste...
In this research, we compare four different evaluation methods in coevolution on the Majority Function problem. The size of the problem is selected such that an evaluation against...
The development of coherent and dynamic behaviors for mobile robots is an exceedingly complex endeavor ruled by task objectives, environmental dynamics and the interactions within...
EVITA, standing for Evolutionary Inventory and Transportation Algorithm, aims to be a commercial tool to address the problem of minimising both the transport and inventory costs o...
In this paper we introduce XCSF with support vector prediction: the problem of learning the prediction function is solved as a support vector regression problem and each classifie...