Parity Codes Used for On-Line Testing in FPGA

This paper deals with on-line error detection in digital circuits implemented in FPGAs. Error detection codes have been used to ensure the self-checking property. The adopted fault model is discussed. A fault in a given combinational circuit must be detected and signalized at the time of its appearance and before further distribution of errors. Hence safe operation of the designed system is guaranteed. The check bits generator and the checker were added to the original combinational circuit to detect an error during normal circuit operation. This concurrent error detection ensures the Totally Self-Checking property. Combinational circuit benchmarks have been used in this work in order to compute the quality of the proposed codes. The description of the benchmarks is based on equations and tables. All of our experimental results are obtained by XILINX FPGA implementation EDA tools. A possible TSC structure consisting of several TSC blocks is presented.

Symbolic regression driven by training data and prior knowledge

In symbolic regression, the search for analytic models is typically driven purely by the prediction error observed on the training data samples. However, when the data samples do not sufficiently cover the input space, the prediction error does not provide sufficient guidance toward desired models. Standard symbolic regression techniques then yield models that are partially incorrect, for instance, in terms of their steady-state characteristics or local behavior. If these properties were considered already during the search process, more accurate and relevant models could be produced. We propose a multi-objective symbolic regression approach that is driven by both the training data and the prior knowledge of the properties the desired model should manifest. The properties given in the form of formal constraints are internally represented by a set of discrete data samples on which candidate models are exactly checked. The proposed approach was experimentally evaluated on three test problems with results clearly demonstrating its capability to evolve realistic models that fit the training data well while complying with the prior knowledge of the desired model characteristics at the same time. It outperforms standard symbolic regression by several orders of magnitude in terms of the mean squared deviation from a reference model.Learning & Autonomous Contro

A hyper-heuristic with a round robin neighbourhood selection

An iterative selection hyper-heuristic passes a solution through a heuristic selection process to decide on a heuristic to apply from a fixed set of low level heuristics and then a move acceptance process to accept or reject the newly created solution at each step. In this study, we introduce Robinhood hyper-heuristic whose heuristic selection component allocates equal share from the overall execution time for each low level heuristic, while the move acceptance component enables partial restarts when the search process stagnates. The proposed hyper-heuristic is implemented as an extension to a public software used for benchmarking of hyper-heuristics, namely HyFlex. The empirical results indicate that Robinhood hyper-heuristic is a simple, yet powerful and general multistage algorithm performing better than most of the previously proposed selection hyper-heuristics across six different Hyflex problem domains