Optimal noise in a stochastic model for local search

A novel local search method for microaggregation

In this paper, we propose an effective microaggregation algorithm to produce a more useful protected data for publishing. Microaggregation is mapped to a clustering problem with known minimum and maximum group size constraints. In this scheme, the goal is to cluster n records into groups of at least k and at most 2k_1 records, such that the sum of the within-group squ...

Teaching Stochastic Local Search

This paper outlines an experiential approach to teaching stochastic local search. Using the Tale of the Drunken Topographer as a running analogy, students are led from the implementation of a hill descent algorithm through small, motivated modifications to a simple implementation of simulated annealing. Supplementary applets allow students to experiment with temperature and gain understanding o...

Stochastic Local Search Algorithms

The Genomic Median Problem is an optimization problem inspired by a biological issue: it aims at finding the genome organization of the common ancestor to multiple living species. It is formulated as the search for a genome that minimizes some distance measure among given genomes. Several attempts have been made at solving the problem. These range from simple heuristic methods to a stochastic l...

On Multiplicative Noise Models for Stochastic Search

In this paper we investigate multiplicative noise models in the context of continuous optimization. We illustrate how some intrinsic properties of the noise model imply the failure of reasonable search algorithms for locating the optimum of the noiseless part of the objective function. Those findings are rigorously investigated on the (1 + 1)-ES for the minimization of the noisy sphere function...

A Stochastic Optimization Model for Groundwater Management