Exact computation of the expectation curves of the bit-flip mutation using landscapes theory

Chicano, F., & Alba E. (2011). Exact computation of the expectation curves of the bit-flip mutation using landscapes theory. Proceedings of 13th Annual Genetic and Evolutionary Computation Conference, Dublin, Ireland, July 12-16, 2011. pp. 2027–2034.Bit-flip mutation is a common operation when a genetic algorithm is applied to solve a problem with binary representation. We use in this paper some results of landscapes theory and Krawtchouk polynomials to exactly compute the expected value of the fitness of a mutated solution. We prove that this expectation is a polynomial in p, the probability of flipping a single bit. We analyze these polynomials and propose some applications of the obtained theoretical results.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. This research has been partially funded by the Spanish Ministry of Science and Innovation and FEDER under contract TIN2008-06491-C04-01 (the M∗ project) and the Andalusian Government under contract P07-TIC-03044 (DIRICOM project)

Computing New Optimized Routes for GPS Navigators Using Evolutionary Algorithms

GPS navigators are now present in most vehicles and smartphones. The usual goal of these navigators is to take the user in less time or distance to a destination. However, the global use of navigators in a given city could lead to traffic jams as they have a highly biased preference for some streets. From a general point of view, spreading the traffic throughout the city could be a way of preventing jams and making a better use of public resources. We propose a way of calculating alternative routes to be assigned by these devices in order to foster a better use of the streets. Our experimentation involves maps from OpenStreetMap, real road traffic, and the microsimulator SUMO. We contribute to reducing travel times, greenhouse gas emissions, and fuel consumption. To analyze the sociological aspect of any innovation, we analyze the penetration (acceptance) rate which shows that our proposal is competitive even when just 10% of the drivers are using it.Spanish MINECO project TIN2014-57341-R (http://moveon.lcc.uma.es). FPU grant (FPU13/00954) from the Spanish Ministry of Education, Culture and Sports. University of Malaga. International Campus of Excellence Andalucia TECH

Red Swarm: Smart Mobility in Cities with EAs

This work presents an original approach to regulate traffic by using an on-line system controlled by an EA. Our proposal uses computational spots with WiFi connectivity located at traffic lights (the Red Swarm), which are used to suggest alternative individual routes to vehicles. An evolutionary algorithm is also proposed in order to find a configuration for the Red Swarm spots which reduces the travel time of the vehicles and also prevents traffic jams. We solve real scenarios in the city of Malaga (Spain), thus enriching the OpenStreetMap info by adding traffic lights, sensors, routes and vehicle flows. The result is then imported into the SUMO traffic simulator to be used as a method for calculating the fitness of solutions. Our results are competitive compared to the common solutions from experts in terms of travel and stop time, and also with respect to other similar proposals but with the added value of solving a real, big instance.Ministerio de Economía y Competitividad y FEDER (TIN2011-28194

An Economist´s guide to the Kalman filter

Almost since its appearance, the Kalman Filter (KF) has been successfully used in control engineering. Unfortunately, most of its important results have been published in engineering journals with language, notation and style proper of engineers. In this paper, we want to present the KF in an attractive way to economists by using information theory and Bayesian inference.

Elementary Landscape Decomposition of the Test Suite Minimization Problem

Chicano, F., Ferrer J., & Alba E. (2011). Elementary Landscape Decomposition of the Test Suite Minimization Problem. In Proceedings of Search Based Software Engineering, Szeged, Hungary, September 10-12, 2011. pp. 48–63.Landscape theory provides a formal framework in which combinatorial optimization problems can be theoretically characterized as a sum of a special kind of landscape called elementary landscape. The decomposition of the objective function of a problem into its elementary components provides additional knowledge on the problem that can be exploited to create new search methods for the problem. We analyze the Test Suite Minimization problem in Regression Testing from the point of view of landscape theory. We find the elementary landscape decomposition of the problem and propose a practical application of such decomposition for the search.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. This research has been partially funded by the Spanish Ministry of Science and Innovation and FEDER under contract TIN2008-06491- C04-01 (the M∗ project) and the Andalusian Government under contract P07- TIC-03044 (DIRICOM project)

Math Oracles: A New Way of Designing Efficient Self-Adaptive Algorithms

In this paper we present a new general methodology to develop self-adaptive methods at a low computational cost. Instead of going purely ad-hoc we de ne several simple steps to include theoretical models as additional information in our algorithm. Our idea is to incorporate the predictive information (future behavior) provided by well-known mathematical models or other prediction systems (the oracle) to build enhanced methods. We show the main steps which should be considered to include this new kind of information into any algorithm. In addition, we actually test the idea on a speci c algorithm, a genetic algorithm (GA). Experiments show that our proposal is able to obtain similar, or even better results when it is compared to the traditional algorithm. We also show the bene ts in terms of saving time and a lower complexity of parameter settings.Universidad de Málaga. Proyecto roadME (TIN2011-28194

Comparing metaheuristic algorithms for error detection in Java programs

Chicano, F., Ferreira M., & Alba E. (2011). Comparing Metaheuristic Algorithms for Error Detection in Java Programs. In Proceedings of Search Based Software Engineering, Szeged, Hungary, September 10-12, 2011. pp. 82–96.Model checking is a fully automatic technique for checking concurrent software properties in which the states of a concurrent system are explored in an explicit or implicit way. The main drawback of this technique is the high memory consumption, which limits the size of the programs that can be checked. In the last years, some researchers have focused on the application of guided non-complete stochastic techniques to the search of the state space of such concurrent programs. In this paper, we compare five metaheuristic algorithms for this problem. The algorithms are Simulated Annealing, Ant Colony Optimization, Particle Swarm Optimization and two variants of Genetic Algorithm. To the best of our knowledge, it is the first time that Simulated Annealing has been applied to the problem. We use in the comparison a benchmark composed of 17 Java concurrent programs. We also compare the results of these algorithms with the ones of deterministic algorithms.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. This research has been partially funded by the Spanish Ministry of Science and Innovation and FEDER under contract TIN2008-06491-C04-01 (the M∗ project) and the Andalusian Government under contract P07-TIC-03044 (DIRICOM project)