The compressed differential heuristic

The Compressed Differential Heuristic

The differential heuristic (DH) is an effective memory-based heuristic for explicit state spaces. In this paper we aim to improve its performance and memory usage. We introduce a compression method for DHs which stores only a portion of the original uncompressed DH, while preserving enough information to enable efficient search. Compressed DHs (CDH) can be tuned to fit any size of memory, even ...

Pattern Matching in Text Compressed with the ID Heuristic

A Compressed-Annealing Heuristic for the Traveling Salesman Problem with Time Windows

This paper describes a variant of simulated annealing incorporating a variable penalty method to solve the traveling salesman problem with time windows (TSPTW). Augmenting temperature from traditional simulated annealing with the concept of pressure (analogous to the value of the penalty multiplier), compressed annealing relaxes the time window constraints by integrating a penalty method within...

Sparse Signal Reconstruction from Compressed Measurements using Hybrid Differential Evolution

One of the main challenges in Compressed Sensing (CS) is the sparse signal reconstruction from less number ofnon-adaptive linear measurements. The recovery process, based on l0 minimization, involves exhaustive search for non-zero entries in the estimated signal thus making it practically intractable. This workpresents a novel technique of recovering a K-sparse signal from compressive measureme...

Compressed Differential Erasure Codes for Efficient Archival of Versioned Data

In this paper, we study the problem of storing an archive of versioned data in a reliable and efficient manner in distributed storage systems. We propose a new storage technique called differential erasure coding (DEC) where the differences (deltas) between subsequent versions are stored rather than the whole objects, akin to a typical delta encoding technique. However, unlike delta encoding te...