How Noisy Data Affects Geometric Semantic Genetic Programming

Noise is a consequence of acquiring and pre-processing data from the environment, and shows fluctuations from different sources---e.g., from sensors, signal processing technology or even human error. As a machine learning technique, Genetic Programming (GP) is not immune to this problem, which the field has frequently addressed. Recently, Geometric Semantic Genetic Programming (GSGP), a semantic-aware branch of GP, has shown robustness and high generalization capability. Researchers believe these characteristics may be associated with a lower sensibility to noisy data. However, there is no systematic study on this matter. This paper performs a deep analysis of the GSGP performance over the presence of noise. Using 15 synthetic datasets where noise can be controlled, we added different ratios of noise to the data and compared the results obtained with those of a canonical GP. The results show that, as we increase the percentage of noisy instances, the generalization performance degradation is more pronounced in GSGP than GP. However, in general, GSGP is more robust to noise than GP in the presence of up to 10% of noise, and presents no statistical difference for values higher than that in the test bed.Comment: 8 pages, In proceedings of Genetic and Evolutionary Computation Conference (GECCO 2017), Berlin, German

1.57 μm InGaAsP/InP surface emitting lasers by angled focus ion beam etching

The characteristics of 1.57 μm InGaAsP/InP surface emitting lasers based on an in-plan ridged structure and 45° beam deflectors defined by angled focused ion beam (FIB) etching are reported. With an externally integrated beam deflector, threshold currents and emission spectra identical to conventional edge emitting lasers are achieved. These results show that FIB etching is a very promising technique for the definition of high quality mirrors and beam deflectors on semiconductor heterostructures for a variety of integrated optoelectronic devices

A rigorous evaluation of crossover and mutation in genetic programming

The role of crossover and mutation in Genetic Programming (GP) has been the subject of much debate since the emergence of the field. In this paper, we contribute new empirical evidence to this argument using a rigorous and principled experimental method applied to six problems common in the GP literature. The approach tunes the algorithm parameters to enable a fair and objective comparison of two different GP algorithms, the first using a combination of crossover and reproduction, and secondly using a combination of mutation and reproduction. We find that crossover does not significantly outperform mutation on most of the problems examined. In addition, we demonstrate that the use of a straightforward Design of Experiments methodology is effective at tuning GP algorithm parameters

Monolithic InP-Based Grating Spectrometer for Wavelength-Division Multiplexed Systems at 1.5 μm

A monolithic InP-based grating spectrometer for use in wavelength-division multiplexed systems at 1.5 μm is reported. The spectrometer uses a single etched reflective focusing diffraction grating and resolves >50 channels at 1 nm spacing with a ~0.3nm channel width and at least 19dB channel isolation. Operation is essentially of the state of the input polarisation

Automatic symbolic modelling of co-evolutionarily learned robot skills

Proceeding of: 6th International Work-Conference on Artificial and Natural Neural Networks, IWANN 2001 Granada, Spain, June 13–15, 2001Evolutionary based learning systems have proven to be very powerful techniques for solving a wide range of tasks, from prediction to optimization. However, in some cases the learned concepts are unreadable for humans. This prevents a deep semantic analysis of what has been really learned by those systems. We present in this paper an alternative to obtain symbolic models from subsymbolic learning. In the first stage, a subsymbolic learning system is applied to a given task. Then, a symbolic classifier is used for automatically generating the symbolic counterpart of the subsymbolic model. We have tested this approach to obtain a symbolic model of a neural network. The neural network defines a simple controller af an autonomous robot. a competitive coevolutive method has been applied in order to learn the right weights of the neural network. The results show that the obtained symbolic model is very accurate in the task of modelling the subsymbolic system, adding to this its readability characteristic

Wavelength-selectable laser emission from a multistripe array grating integrated cavity laser

We report laser operation of a multistripe array grating integrated cavity (MAGIC) laser in which the wavelength of the emission from a single output stripe is chosen by selectively injection pumping a second stripe. We demonstrate a device that lases in the 1.5 µm fiber band at 15 wavelengths, evenly spaced by ~2 nm. The single-output/wavelength-selectable operation, together with the accurate predefinition of the lasing wavelengths, makes the MAGIC laser a very attractive candidate for use in multiwavelength networks