PARPROG - Parallele Prognoseverfahren fuer die Absatzplanung Schlussbericht

1. Current scientific/technical situation: Currently, there are two major approaches to forecasting of economical data: classical statistical methods and Computational Intelligence (CI) approaches (mainly Neural Networks). While the statistical methods depend on expert knowledge in case of non-linear application-specific models, Neural Nets lack the feature of explainability. 2. Motivation/objective of the investigation: Synthesis of statistical and parallel CI methods aiming at an application-specific forecasting tool with explainable results. Due to the high requirements of computation time, efficient parallel implementations are required. 3. Method: Parameter estimation of application-specific statistical forecasting models by means of parallel Evolutionary Algorithms (EAs). Learning of transformations of multi-variate impact time series by means of parallel Genetic Programming (CP). 4. Results: Multi-variate time series models can be successfully estimated. The results concerning the fitting of the estimated data in the past are better than those achieved by expert knowledge and heuristics. The plausibility of the results can be enhanced by restricting the parameter space based on expert knowledge. Due to overfitting, the parallel CP did not improve the explainability of the results. The parallelization of Eas using a modified neighborhood model turned out to be robust and scalable with respect to available communicational and computational capacities. 5. Conclusions/application possibilities: The combination of application-specific models and parallel CI methods can be applied to arbitrary statistical estimation problems. By integrating expert knowledge, this approach leads to a high acceptance of new developments in computer science. Since the parallelization is scalable, the methods are suitable even for small PC networks. (orig.)SIGLEAvailable from TIB Hannover: F99B1154 / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekBundesministerium fuer Bildung und Forschung (BMBF), Bonn (Germany)DEGerman

Growth curves and takeover time in distributed evolutionary algorithms

Abstract. This paper presents a study of different models for the growth curves and takeover time in a distributed EA (dEA). The calculation of the takeover time and the dynamical growth curves is a common analytical approach to measure the selection pressure of an EA. This work is a first step to mathematically unify and describe the roles of the migration rate and the migration frequency in the selection pressure induced by the dynamics of dEAs. In order to achieve these goals we evaluate the appropriateness of the well-known logistic model and of a hypergraph model for dEAs. After that, we propose a corrected hypergraph model and two new models based in an extension of the logistic one. Our results show that accurate models for growth curves can be defined for dEAs, and explain analytically the migration rate and frequency effects.

A unified model of non-panmictic population structures in evolutionary algorithms

SIGLEAvailable from TIB Hannover: RR 8071(99-55)+a / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekDEGerman

Ease - Evolutionary algorithms scripting environment

SIGLEAvailable from TIB Hannover: RR 8071(98-54)+a / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekDEGerman

Intensity-modulated proton therapy, volumetric-modulated arc therapy, and 3D conformal radiotherapy in anaplastic astrocytoma and glioblastoma : A dosimetric comparison.

Purpose: The prognosis for high-grade glioma (HGG) patients is poor; thus, treatment-related side effects need to be minimized to conserve quality of life and functionality. Advanced techniques such as proton radiation therapy (PRT) and volumetric-modulated arc therapy (VMAT) may potentially further reduce the frequency and severity of radiogenic impairment. Materials and methods: We retrospectively assessed 12 HGG patients who had undergone postoperative intensity-modulated proton therapy (IMPT). VMAT and 3D conformal radiotherapy (3D-CRT) plans were generated and optimized for comparison after contouring crucial neuronal structures important for neurogenesis and neurocognitive function. Integral dose (ID), homogeneity index (HI), and inhomogeneity coefficient (IC) were calculated from dose statistics. Toxicity data were evaluated. Results: Target volume coverage was comparable for all three modalities. Compared to 3D-CRT and VMAT, PRT showed statistically significant reductions (p < 0.05) in mean dose to whole brain (−20.2 %, −22.7 %); supratentorial (−14.2 %, −20,8 %) and infratentorial (−91.0 %, −77.0 %) regions; brainstem (−67.6 %, −28.1 %); pituitary gland (−52.9 %, −52.5 %); contralateral hippocampus (−98.9 %, −98.7 %); and contralateral subventricular zone (−62.7 %, −66.7 %, respectively). Fatigue (91.7 %), radiation dermatitis (75.0 %), focal alopecia (100.0 %), nausea (41.7 %), cephalgia (58.3 %), and transient cerebral edema (16.7 %) were the most common acute toxicities. Conclusion: Essential dose reduction while maintaining equal target volume coverage was observed using PRT, particularly in contralaterally located critical neuronal structures, areas of neurogenesis, and structures of neurocognitive functions. These findings were supported by preliminary clinical results confirming the safety and feasibility of PRT in HGG

GEPROG - Genetisches Programmieren fuer Modellierung und Regelung dynamischer Systeme Schlussbericht

Available from TIB Hannover: F02B711+a / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekSIGLEBundesministerium fuer Bildung und Forschung, Berlin (Germany)DEGerman