Category Archives: Technical reports

This paper presents a local search method for the Bayesian optimization algorithm (BOA) based on the concepts of substructural neighborhoods and loopy belief propagation. The proba- bilistic model of BOA, which automatically identifies important problem substructures, is used to define … Continue reading →

Evolutionary algorithms (EAs) are particularly suited to solve problems for which there is not much information available. From this standpoint, estimation of distribution algorithms (EDAs), which guide the search by using probabilistic models of the population, have brought a new … Continue reading →

This paper shows how the extended compact genetic algorithm can be scaled using data- intensive computing techniques such as MapReduce. Two different frameworks (Hadoop and MongoDB) are used to deploy MapReduce implementations of the compact and extended com- pact genetic … Continue reading →

We applied on-line neuroevolution to evolve non-player characters for The Open Racing Car Simulator. While previous approaches allowed on-line learning with performance improvements during each generation, our approach enables a finer grained on-line learning with performance improvements within each lap. … Continue reading →

Abstract: Genetic algorithms(GAs) are increasingly being applied to large scale problems. The traditional MPI-based parallel GAs do not scale very well. MapReduce is a powerful abstraction developed by Google for making scalable and fault tolerant applications. In this paper, we mould … Continue reading →

Proposed is an automatic system for creating pattern generators and recognizers that may provide new and human-independent insight into the pattern recognition problem. The system is based on a three-cornered coevolution of image-transformation programs.

The XCS Library (XCSLib) is an open source C++ library for genetics-based machine learning and learning classifier systems. It provides (i) several reusable components that can be employed to design new learning paradigms inspired to the learning classifier system principles; … Continue reading →

Abstract: Learning Classifier Systems (LCSs) are rule-based systems that can be manipulated by a genetic algorithm. LCSs were first designed by Holland to solve classification problems and a lot of effort has been made since then, resulting in a broad … Continue reading →

Abstract: One of the main problems that arises when using gene expression programming conditions in learning classifier systems is the increasing number of symbols present as the problem size grows. This issue severely limits the scalability of the technique, due … Continue reading →

Abstract:  Genetic Algorithms are a class of metaheuristics with applications on several fields including biology, engineering and even arts. However, simple Genetic Algorithms may suffer from exponential scalability on hard problems. Estimation of Distribution Algorithms, a special class of Genetic … Continue reading →