Circuit Design Based on Particle Swarm Optimization Algorithms

This work investigates the application of Particle Swarm Optimization (PSO) algorithms in the field of evolutionary electronics. PSO was developed under the inspiration of behavior laws of bird flocks, fish schools and human communities. PSO achieves its optimum solution by starting from a group of random solution and then searching repeatedly. We propose the new means for designing electronic circuits and introduce the modified PSO algorithm. For the case studies this means has proved to be efficient, experiments show that we have better results.

A Contribution to Reconfigurable Analog Electronics by a Digitally Programmable Sensor Signal Amplifier

In particular, primary sensor electronics are prone to deviations and degradation in its performance due to environmental influences and manufacturing conditions. In order to restore its functionality, calibration or trimming techniques are usually employed. More recent, programmable or reconfigurable approaches from the field of evolutionary electronics offer great source of inspiration through their unique properties of fault-tolerance and self-repair. In our approach, we try to include efficiently, the available knowledge of recent reconfigurable devices into the otherwise attractive concept. In our approach, a flexible FPTA architecture is developed meeting the requirement of sensor signal amplifier in particular for time continuous signal processing. The developed approach is verified and implemented in 0.35μm CMOS technology.

Fitness Landscapes and Evolvability

In this paper, we develop techniques based on evolvability statistics of the fitness land-scape surrounding sampled solutions. Averaging the measures over a sample of equal fitness solutions allows us to build up fitness evolvability portraits of the fitness land-scape, which we show can be used to compare both the ruggedness and neutrality in a set of tunably rugged and tunably neutral landscapes. We further show that the tech-niques can be used with solution samples collected through both random sampling of the landscapes and online sampling during optimization. Finally, we apply the techniques to two real evolutionary electronics search spaces and highlight differences between the two search spaces, comparing with the time taken to find good solutions through search.

Variable Length Representation in Evolutionary Electronics

This work investigates the application of variable length representation (VLR) evolutionary algorithms (EAs) in the field of Evolutionary Electronics. We propose a number of VLR methodologies that can cope with the main issues of variable length evolutionary systems. These issues include the search for efficient ways of sampling a genome space with varying dimensionalities, the task of balancing accuracy and parsimony of the solutions, and the manipulation of non-coding segments. We compare the performance of three proposed VLR approaches to sample the genome space: Increasing Length Genotypes, Oscillating Length Genotypes, and Uniformly Distributed Initial Population strategies. The advantages of reusing genetic material to replace non-coding segments are also emphasized in this work. It is shown, through examples in both analog and digital electronics, that the variable length genotype's representation is natural to this particular domain of application. A brief discussion on biological genome evolution is also provided.