Christos Ampatzis

Parallel global optimisation meta-heuristics using an asynchronous island-model

We propose an asynchronous island-model algorithm distribution framework and test the popular Differential Evolution algorithm performance when a few processors are available. We confirm that the island-model introduces the possibility of creating new algorithms consistently going beyond the performances of parallel Differential Evolution multi starts. Moreover, we suggest that using heterogeneous strategies along different islands consistently reaches the reliability and performance of the best of the strategies involved, thus alleviating the problem of algorithm selection. We base our conclusions on experiments performed on high dimensional standard test problems (Rosenbrock 100, Rastrigin 300, Lennard Jones 10 atoms), but also, remarkably, on complex spacecraft interplanetary trajectory optimisation test problems (Messenger, Cassini, GTOC1). Spacecraft trajectory global optimisation problems have been recently proposed as hard benchmark problems for continuous global optimisation. High computational resources needed to tackle these type of problems make them an ideal playground for the development and testing of high performance computing algorithms based on multiple processor availability.

Implicit retrieval of salient images using Brain Computer Interface

Space missions are often equipped with several high definition sensors that can autonomously collect a potentially enormous amount of data. The bottleneck in retrieving these often precious datasets is the onboard data storing capability and the communication bandwidth, which limit the amount of data that can be sent back to Earth. In this paper, we propose a method based on the analysis of brain electrical activity to identify the scientific interest of experts towards a given image in a large set of images. Such a method can be used to efficiently create an abundant training set (images and whether they are scientifically interesting) with a considerably faster image presentation rate that can go beyond expert consciousness, with less interrogation time for experts and relatively high performance.

Alife in the galapagos: migration effects on neuro-controller design

The parallelization of evolutionary computation tasks using a coarse-grained approach can be efficiently achieved using the island migration model. Strongly influenced by the theory of punctuated equilibria, such a scheme guarantees an efficient exchange of genetic material between niches, not only accelerating but also improving the evolutionary process. We study the island model computational paradigm in relation to the evolutionary robotics methodology. We let populations of robots evolve in different islands of an archipelago and exchange individuals along allowed migration paths. We show, for the test-case selected, how the exchange of genetic material coming from different islands improves the overall design efficiency and speed, effectively taking advantage of a parallel computing environment to improve the methodology of evolutionary robotics, often criticized for its computational cost.

The impact of expertise on brain computer interface based salient image retrieval

Autonomous decision making modules in computer vision application allow recognition and classification of different objects, persons, and events in images and video sequences and also make it possible to classify different sensor readings (e.g. images) according to their scientific saliencies. In this paper, we propose a new approach to create the training set for these algorithms by retrieving salient images using electroencephalogram (EEG) and brain computer interface (BCI) and rapid image presentation. To this end, two groups of subjects, namely, expert and novice subjects were asked to participate in our experiments. We show that a relatively high retrieval accuracy can be achieved for most of the subjects. Furthermore, to assess the impact of expertise on the retrieval process, we study their EEG signals separately and show that there is a clear difference in their brainwaves while observing salient images.

To grip, or not to grip: evolving coordination in autonomous robots

In evolutionary robotics, as in the animal world, performing a task which is beneficial to the entire group demands the coordination of different individuals. Whenever time-dependent dynamic allocation of roles is needed and individual roles are not pre-defined, coordination can often be hard to achieve. In this paper, we study the evolution of role allocation and self-assembling strategies in a group of two homogeneous robots.We show how robot coordination and individual choices (who will grip whom) can be successfully restated in terms of anti-coordination problems, showing how conventional game theoretical tools can be used in the interpretation and design of evolutionary outcomes in collective robotics. Moreover, we highlight and discuss striking similarities between the way our physical robots allocate roles and the way animals solve conflicts. Arguably, these similarities suggest that evolutionary robotics may offer apart from automatic controller design for autonomous robots a viable alternative for the study of biological phenomena.

An island-model framework for evolving neuro-controllers for planetary rover control

Autonomous navigation and robust obstacle avoidance are prerequisites for the successful operation of a planetary rover. Typical approaches to tackling this problem rely on complex and computationally expensive navigation strategies based upon the creation of 3D maps of the environment. In contrast, this research proposes a simple artificial neural network relying on infrared sensory input as the control structure. This paper presents a unified framework for designing such control structures for a simulated rover, taking advantage of code parallelisation and the latest advances in global optimisation research. In particular, it details a 3D physics-based simulation of a planetary rover and a tool set for performing the optimisation of ANN parameters within the island model. This paper also presents preliminary results showing that the aforementioned framework can parallelise the controller design process without any loss in performance over traditional methods, and will outline research directions, which aim to take full advantage of this techniques potential.