A general learning algorithm for solving optimization problems and its application to the spin glass problem

We propose a general learning algorithm for solving optimization problems, based on a simple strategy of trial and adaptation. The algorithm maintains a probability distribution of possible solutions (configurations), which is updated continuously in the learning process. As the probability distribution evolves, better and better solutions are shown to emerge. The performance of the algorithm is illustrated by the application to the problem of finding the ground state of the Ising spin glass. A simple theoretical understanding of the algorithm is also presented.Comment: 9 pages, 3 figure

Prevention and control of contaminants of industrial processes and pesticides in the poultry production chain

The reduction in levels of organochlorine pesticide residues in food of animal origin in the past 30 years has been achieved especially by controlling entrance via the feed chain. A further reduction was achieved by registration and use of less persistent pesticides both for direct treatment of animals and of plant material. The remaining problems (e.g. dioxins and PCB's) are much harder to tackle. They are either of a ubiquitous nature and their impact might be enlarged by the present welfare trend requiring more contact of the animals with their environment, or they are of a sporadic nature making checking and control quite hard to execute. The present public demand for a farm animal production that is in balance with the animals' needs and a residue free product adds even more complications to the system