In reinforcement learning, an agent interacting with its environment strives to learn a policy that specifies, for each state it may encounter, what action to take. Evolutionary c...
In this paper we embed evolutionary computation into statistical learning theory. First, we outline the connection between large margin optimization and statistical learning and s...
In this paper we set the first steps towards the development of a commercially viable tool that uses evolutionary computation to address the Product to Shelf Allocation Problem (P...
Since the genomics revolution, bioinformatics has never been so popular. Many researchers have investigated with great success the use of evolutionary computation in bioinformatic...
Recently, evolutionary computation has been successfully integrated into statistical learning methods. A Support Vector Machine (SVM) using evolution strategies for its optimizati...
Learning genetic representation has been shown to be a useful tool in evolutionary computation. It can reduce the time required to find solutions and it allows the search process ...
Abstract. Perhaps one the newest and of the more interesting cooperative approaches to evolutionary computation which has been more recently explored is the area of mutualism. In m...
This paper explores the use of growth processes, or embryogenies, to map genotypes to phenotypes within evolutionary systems. Following a summary of the significant features of em...
Evolving solutions rather than computing them certainly represents an unconventional programming approach. The general methodology of evolutionary computation has already been know...
We have already proposed using evolutionary computation to adjust the voice quality conversion parameters, and we have reported that this approach produces results that are not onl...