60 research outputs found
A Classification of Hyper-heuristic Approaches
The current state of the art in hyper-heuristic research comprises a set of approaches that share the common goal of automating the design and adaptation of heuristic methods to solve hard computational search problems. The main goal is to produce more generally applicable search methodologies. In this chapter we present and overview of previous categorisations of hyper-heuristics and provide a unified classification and definition which captures the work that is being undertaken in this field. We distinguish between two main hyper-heuristic categories: heuristic selection and heuristic generation. Some representative examples of each category are discussed in detail. Our goal is to both clarify the main features of existing techniques and to suggest new directions for hyper-heuristic research
Fuzzy Dynamical Genetic Programming in XCSF
A number of representation schemes have been presented for use within
Learning Classifier Systems, ranging from binary encodings to Neural Networks,
and more recently Dynamical Genetic Programming (DGP). This paper presents
results from an investigation into using a fuzzy DGP representation within the
XCSF Learning Classifier System. In particular, asynchronous Fuzzy Logic
Networks are used to represent the traditional condition-action production
system rules. It is shown possible to use self-adaptive, open-ended evolution
to design an ensemble of such fuzzy dynamical systems within XCSF to solve
several well-known continuous-valued test problems.Comment: 2 page GECCO 2011 poster pape
Discrete and fuzzy dynamical genetic programming in the XCSF learning classifier system
A number of representation schemes have been presented for use within
learning classifier systems, ranging from binary encodings to neural networks.
This paper presents results from an investigation into using discrete and fuzzy
dynamical system representations within the XCSF learning classifier system. In
particular, asynchronous random Boolean networks are used to represent the
traditional condition-action production system rules in the discrete case and
asynchronous fuzzy logic networks in the continuous-valued case. It is shown
possible to use self-adaptive, open-ended evolution to design an ensemble of
such dynamical systems within XCSF to solve a number of well-known test
problems
A Cloud robotics architecture to foster individual child partnership in medical facilities
Robots and automation systems have become a
valuable partner in several facets of human life: from learning
and teaching, to daily working, including health monitoring
and assistance. So far, these appealing robot-based applications
are restricted to conduct repetitive, yet useful, tasks due to the
reduced individual robots’ capabilities in terms of processing
and computation. This concern prevents current robots from
facing more complex applications related to understanding hu-
man beings and perceiving their subtle feelings. Such hardware
limitations have been already found in the computer science
field. In this domain, they are currently being addressed using
a new resource exploitation model coined as cloud computing,
which is targeted at enabling massive storage and computation
using smartly connected and inexpensive commodity hardware.
The purpose of this paper is to propose a cloud-based robotics
architecture to effectively develop complex tasks related to
hospitalized children assistance. More specifically, this paper
presents a multi-agent learning system that combines machine
learning and cloud computing using low-cost robots to (1)
collect and perceive children status, (2) build a human-readable
set of rules related to the child-robot relationship, and (3)
improve the children experience during their stay in the hos-
pital. Conducted preliminary experiments proof the feasibility
of this proposal and encourage practitioners to work towards
this direction.Peer ReviewedPostprint (published version
- …