Revisiting the Edge of Chaos: Evolving Cellular Automata to Perform Computations

We present results from an experiment similar to one performed by Packard (1988), in which a genetic algorithm is used to evolve cellular automata (CA) to perform a particular computational task. Packard examined the frequency of evolved CA rules as a function of Langton's lambda parameter (Langton, 1990), and interpreted the results of his experiment as giving evidence for the following two hypotheses: (1) CA rules able to perform complex computations are most likely to be found near ``critical'' lambda values, which have been claimed to correlate with a phase transition between ordered and chaotic behavioral regimes for CA; (2) When CA rules are evolved to perform a complex computation, evolution will tend to select rules with lambda values close to the critical values. Our experiment produced very different results, and we suggest that the interpretation of the original results is not correct. We also review and discuss issues related to lambda, dynamical-behavior classes, and computation in CA. The main constructive results of our study are identifying the emergence and competition of computational strategies and analyzing the central role of symmetries in an evolutionary system. In particular, we demonstrate how symmetry breaking can impede the evolution toward higher computational capability.Comment: 38 pages, compressed .ps files (780Kb) available ONLY thru anonymous ftp. (Instructions available via `get 9303003' .

Myth and Reality of a Universal Lithium-Ion Battery Electrode Design Optimum: A Perspective and Case Study

The quest toward optimal electrode design for energy‐ and power‐demanding applications involves besides experimental effort also less resource‐intensive model‐based studies. The diversity of optimization objectives and benchmark systems complicates the practical utilization of available methods and gained knowledge. Despite the increasing importance of fast charging, electrode design studies commonly focus only on discharge characteristics. This paper features, besides an overview and perspective of electrode structuring concepts and optimization pathways, a model‐based full cell parameter screening of two‐layered electrodes for charge and discharge. The small fraction of cells with superior performance among the evaluated configurations underlines the importance of a joint experimental and model‐based electrode design optimization. The results further indicate that the performance of cell designs tailored for fast charge or fast discharge differs substantially; the gap widens if charging is terminated below 0 V versus Li/Li+ to prevent lithium plating. The broad parameter screening is complemented by a high‐resolution half cell parameter study. Their comparison underlines that the benefit of electrode structuring depends heavily on the study extent and the chosen benchmark. Furthermore, the importance of the parameter space surrounding an optimal electrode design for production with process tolerances is highlighted

A neural network architecture for data editing in the Bank of ItalyÂ’s business surveys

This paper presents an application of neural network models to predictive classification for data quality control. Our aim is to identify data affected by measurement error in the Bank of ItalyÂ’s business surveys. We build an architecture consisting of three feed-forward networks for variables related to employment, sales and investment respectively: the networks are trained on input matrices extracted from the error-free final survey database for the 2003 wave, and subjected to stochastic transformations reproducing known error patterns. A binary indicator of unit perturbation is used as the output variable. The networks are trained with the Resilient Propagation learning algorithm. On the training and validation sets, correct predictions occur in about 90 per cent of the records for employment, 94 per cent for sales, and 75 per cent for investment. On independent test sets, the respective quotas average 92, 80 and 70 per cent. On our data, neural networks perform much better as classifiers than logistic regression, one of the most popular competing methods, on our data. They appear to provide a valid means of improving the efficiency of the quality control process and, ultimately, the reliability of survey data.data quality, data editing, binary classification, neural networks, measurement error