Genetic local search for multicast routing with pre-processing by logarithmic simulated annealing

Over the past few years, several local search algorithms have been proposed for various problems related to multicast routing in the off-line mode. We describe a population-based search algorithm for cost minimisation of multicast routing. The algorithm utilises the partially mixed crossover operation (PMX) under the elitist model: for each element of the current population, the local search is based upon the results of a landscape analysis that is executed only once in a pre-processing step; the best solution found so far is always part of the population. The aim of the landscape analysis is to estimate the depth of the deepest local minima in the landscape generated by the routing tasks and the objective function. The analysis employs simulated annealing with a logarithmic cooling schedule (logarithmic simulated annealing—LSA). The local search then performs alternating sequences of descending and ascending steps for each individual of the population, where the length of a sequence with uniform direction is controlled by the estimated value of the maximum depth of local minima. We present results from computational experiments on three different routing tasks, and we provide experimental evidence that our genetic local search procedure that combines LSA and PMX performs better than algorithms using either LSA or PMX only

Adaptive simulated annealing for CT image classification

This paper presents a pattern classification method that combines the classical Perceptron algorithm with simulated annealing. The approach is applied to the recognition of focal liver tumors presented in the DICOM format. On test sets of 100+100 examples (disjoint from the learning set) we obtain a correct classification of more than 98%. This work was carried out as part of a collaboration with medical practitioners based at the Institute of Radiology, Humboldt University of Berlin. This paper builds upon work first presented at ESANN 2001

Depth-Four Threshold Circuits for Computer-Assisted X-ray Diagnosis

The paper continues our research from [1]. We present a stochastic algorithm that computes threshold circuits designed to discriminate between two classes of CT images. The algorithm is evaluated for the case of liver tissue classification. A depth-four threshold circuit is calculated from 400 positive (abnormal findings) and 400 negative (normal liver tissue) examples. The examples are of size n = 14161 = 119 x 119 with an 8 bit grey scale. On test sets of 100 + 100 examples (all different from the learning set) we obtain a correct classification of about 96%. The classification of a single image is performed within a few seconds