Jose Torres-Jimenez

An effective two-stage simulated annealing algorithm for the minimum linear arrangement problem

In this paper, an improved two-stage simulated annealing algorithm is presented for the minimum linear arrangement problem for graphs. This algorithm integrates several distinguished features including an efficient heuristic to generate good quality initial solutions, a highly discriminating evaluation function, a special neighborhood function and an effective cooling schedule. The algorithm is evaluated on a set of 30 well-known benchmark instances of the literature and compared with several state-of-the-art algorithms, showing improvements of 17 previous best results.

New bounds for binary covering arrays using simulated annealing

Covering arrays (CAs) are combinatorial structures specified as a matrix of N rows and k columns over an alphabet on v symbols such that for each set of t columns (called the strength of the array) every t-tuple of symbols is covered. Recently they have been used to represent interaction test suites for software testing given that they provide economical sized test cases while still preserving important fault detection capabilities. This paper introduces an improved implementation of a simulated annealing algorithm, called ISA, for constructing CAs of strengths three through six over a binary alphabet (i.e., binary CAs). Extensive experimentation is carried out, using 127 well-known benchmark instances, for assessing its performance with respect to an existing simulated annealing implementation, a greedy method, and five state-of-the-art algorithms. The results show that our algorithm attains 104 new bounds and equals the best-known solutions for the other 23 instances consuming reasonable computational time. Furthermore, the implications of using these results as ingredients to recursive constructions are also analyzed.

An Improved Simulated Annealing Algorithm for Bandwidth Minimization

In this paper, a simulated annealing algorithm is presented for the Bandwidth Minimization Problem for Graphs. This algorithm is based on three distinguished features including an original internal representation of solutions, a highly discriminating evaluation function and an effective neighborhood. The algorithm is evaluated on a set of 113 well-known benchmark instances of the literature and compared with several state-of-the-art algorithms, showing improvements of some previous best results.

A grouping genetic algorithm with controlled gene transmission for the bin packing problem

In this study, the one-dimensional Bin Packing Problem (BPP) is approached. The BPP is a classical optimization problem that is known for its applicability and complexity. We propose a method that is referred to as the Grouping Genetic Algorithm with Controlled Gene Transmission (GGA-CGT) for Bin Packing. The proposed algorithm promotes the transmission of the best genes in the chromosomes without losing the balance between the selective pressure and population diversity. The transmission of the best genes is accomplished by means of a new set of grouping genetic operators, while the evolution is balanced with a new reproduction technique that controls the exploration of the search space and prevents premature convergence of the algorithm. The results obtained from an extensive computational study confirm that (1) promoting the transmission of the best genes improves the performance of each grouping genetic operator; (2) adding intelligence to the packing and rearrangement heuristics enhances the performance of a GGA; (3) controlling selective pressure and population diversity tends to lead to higher effectiveness; and (4) GGA-CGT is comparable to the best state-of-the-art algorithms, outperforming the published results for the class of instances Hard28, which appears to have the greatest degree of difficulty for BPP algorithms.

Construction of mixed covering arrays of variable strength using a tabu search approach

The development of a new system involves extensive tests on the software functionality in order to identify possible failures. Also, a system already built requires a fine tuning of its configurable options to give the best performance in the environment it is going to work. Both cases require a finite set of tests that avoids testing all the possible combinations (which is time consuming); to this situation Mixed Covering Arrays (MCAs) are a feasible alternative. MCAs are combinatorial structures represented as matrices having a test case per row. MCAs are small, in comparison with brute force, and guarantees a level of interaction among the parameters involved (a difference with random testing). We present a Tabu Search (TS) algorithm to construct MCAs; the novelty in the algorithm is a mixture of three neighborhood functions. We also present a new benchmark for the MCAs problem. The experimental evidence showed that the TS algorithm improves the results obtained by other approaches reported in the literature, finding the optimal solution in some the solved cases.

Memetic algorithms for constructing binary covering arrays of strength three

This paper presents a new Memetic Algorithm (MA) designed to compute near-optimal solutions for the covering array construction problem. It incorporates several distinguished features including an efficient heuristic to generate a good quality initial population, and a local search operator based on a fine tuned Simulated Annealing (SA) algorithm employing a carefully designed compound neighborhood. Its performance is investigated through extensive experimentation over well known benchmarks and compared with other state-of-the-art algorithms, showing improvements on some previous best-known results.

Strength Two Covering Arrays Construction Using a SAT Representation

According to the NIST report of 2002 there is a great potential to reduce the cost, and to increase the quality of the software developed in USA through the creation of automated tools that help in the software testing process. One alternative to improve the software testing process is the creation of tools that generate testing cases in an automatic way. Through the construction of Covering Arrays (CA) it is possible to obtain a minimum set of test cases with the maximum possibility of testing all the functionality of the developed software. In this paper an approach to construct CA using the propositional satisfiability problem (SAT) is presented. The approach starts with the transformation of a CA instance into a non-conjunctive normal form (non-CNF) SAT instance. Then the SAT instance is solved through a non-CNF SAT solver, and finally the SAT solution is transformed into a CA solution. The main contributions of this work are: an efficient non-CNF SAT solver able to equals or improves previously reported results, and a simplified SAT representation to solve the CA problem.

A New Backtracking Algorithm for Constructing Binary Covering Arrays of Variable Strength

A Covering Array denoted by CA(N ;t ,k ,v ) is a matrix of size N ×k , in which each of the v t combinations appears at least once in every t columns. Covering Arrays (CAs) are combinatorial objects used in software testing. There are different methods to construct CAs, but as it is a highly combinatorial problem, few complete algorithms to construct CAs have been reported. In this paper a new backtracking algorithm based on the Branch & Bound technique is presented. It searches only non-isomorphic Covering Arrays to reduce the search space of the problem of constructing them. The results obtained with this algorithm are able to match some of the best known solutions for small instances of binary CAs.

Exciting FPGA cryptographic Trojans using combinatorial testing

Contemporary hardware design shares many similarities with software development. The injection of malicious functionality (Trojans) in FPGA designs is a realistic threat. Established techniques for testing correctness do not cope well with Trojans, since Trojans are not captured in the system model. Furthermore, a well-designed Trojan activates under rare conditions and can escape detection during testing. Such conditions cannot be exhaustively searched, especially in the case of cryptographic core implementations with hundreds of inputs. In this paper, we explore the applicability of a prominent combinatorial strategy, namely combinatorial testing, for FPGA Trojan detection. We demonstrate that combinatorial testing provides the theoretical guarantees for exciting a Trojan of specific lengths by covering all input combinations. Our findings indicate that combinatorial testing constructs can improve the existing FPGA Trojan detection capabilities by reducing significantly the number of tests needed. Besides the foundations of our approach, we also report on first experiments that indicate its practical use.

Simulated Annealing for constructing binary covering arrays of variable strength

This paper presents new upper bounds for binary covering arrays of variable strength constructed by using a new Simulated Annealing (SA) algorithm. This algorithm incorporates several distinguished features including an efficient heuristic to generate good quality initial solutions, a compound neighborhood function which combines two carefully designed neighborhoods and a fine-tuned cooling schedule. Its performance is investigated through extensive experimentation over well known benchmarks and compared with other state-of-the-art algorithms, showing that the proposed SA algorithm is able to outperform them.