José E. Gallardo’s research while affiliated with University of Malaga and other places

We consider an operational model of suicide bombing attacks -- an increasingly prevalent form of terrorism -- against specific targets, and the use of protective countermeasures based on the deployment of detectors over the area under threat. These detectors have to be carefully located in order to minimize the expected number of casualties or the economic damage suffered, resulting in a hard optimization problem for which different metaheuristics have been proposed. Rather than assuming random decisions by the attacker, the problem is approached by considering different models of the latter, whereby he takes informed decisions on which objective must be targeted and through which path it has to be reached based on knowledge on the importance or value of the objectives or on the defensive strategy of the defender (a scenario that can be regarded as an adversarial game). We consider four different algorithms, namely a greedy heuristic, a hill climber, tabu search and an evolutionary algorithm, and study their performance on a broad collection of problem instances trying to resemble different realistic settings such as a coastal area, a modern urban area, and the historic core of an old town. It is shown that the adversarial scenario is harder for all techniques, and that the evolutionary algorithm seems to adapt better to the complexity of the resulting search landscape.

The problem of finding the optimal placement of emergency exits in an indoor environment to facilitate the rapid and orderly evacuation of crowds is addressed in this work. A cellular-automaton model is used to simulate the behavior of pedestrians in such scenarios, taking into account factors such as the environment, the pedestrians themselves, and the interactions among them. A metric is proposed to determine how successful or satisfactory an evacuation was. Subsequently, two metaheuristic algorithms, namely an iterated greedy heuristic and an evolutionary algorithm (EA) are proposed to solve the optimization problem. A comparative analysis shows that the proposed EA is able to find effective solutions for different scenarios, and that an island-based version of it outperforms the other two algorithms in terms of solution quality.

Suicide bombing is an infamous form of terrorism that is becoming increasingly prevalent in the current era of global terror warfare. We consider the case of targeted attacks of this kind, and the use of detectors distributed over the area under threat as a protective countermeasure. Such detectors are non-fully reliable, and must be strategically placed in order to maximize the chances of detecting the attack, hence minimizing the expected number of casualties. To this end, different metaheuristic approaches based on local search and on population-based search are considered and benchmarked against a powerful greedy heuristic from the literature. We conduct an extensive empirical evaluation on synthetic instances featuring very diverse properties. Most metaheuristics outperform the greedy algorithm, and a hill-climber is shown to be superior to remaining approaches. This hill-climber is subsequently subject to a sensitivity analysis to determine which problem features make it stand above the greedy approach, and is finally deployed on a number of problem instances built after realistic scenarios, corroborating the good performance of the heuristic.

The FAR FROM MOST STRING PROBLEM (FFMSP) is a string selection problem. The objective is to find a string whose distance to other strings in a certain input set is above a given threshold for as many of those strings as possible. This problem has links with some tasks in computational biology and its resolution has been shown to be very hard. We propose a memetic algorithm (MA) to tackle the FFMSP. This MA exploits a heuristic objective function for the problem and features initialization of the population via a Greedy Randomized Adaptive Search Procedure (GRASP) metaheuristic, intensive recombination via path relinking and local improvement via hill climbing. An extensive empirical evaluation using problem instances of both random and biological origin is done to assess parameter sensitivity and draw performance comparisons with other state-of-the-art techniques. The MA is shown to perform better than these latter techniques with statistical significance.

The problem of finding the optimal placement of emergency exits in an indoor environment to facilitate the rapid and orderly evacuation of crowds is addressed in this work. A cellular-automaton model is used to simulate the behavior of pedestrians in such scenarios, taking into account factors such as the environment, the pedestrians themselves, and the interactions among them. A metric is proposed to determine how successful or satisfactory an evacuation was. Subsequently, two metaheuristic algorithms, namely an iterated greedy heuristic and an evolutionary algorithm (EA) are proposed to solve the optimization problem. A comparative analysis shows that the proposed EA is able to find effective solutions for different scenarios, and that an island-based version of it outperforms the other two algorithms in terms of solution quality.

We consider an operational model of suicide bombing attacks—an increasingly prevalent form of terrorism—against specific targets, and the use of protective countermeasures based on the deployment of detectors over the area under threat. These detectors have to be carefully located in order to minimize the expected number of casualties or the economic damage suffered, resulting in a hard optimization problem for which different metaheuristics have been proposed. Rather than assuming random decisions by the attacker, the problem is approached by considering different models of the latter, whereby he takes informed decisions on which objective must be targeted and through which path it has to be reached based on knowledge on the importance or value of the objectives or on the defensive strategy of the defender (a scenario that can be regarded as an adversarial game). We consider four different algorithms, namely a greedy heuristic, a hill climber, tabu search and an evolutionary algorithm, and study their performance on a broad collection of problem instances trying to resemble different realistic settings such as a coastal area, a modern urban area, and the historic core of an old town. It is shown that the adversarial scenario is harder for all techniques, and that the evolutionary algorithm seems to adapt better to the complexity of the resulting search landscape.

Each instance contains n strings (possibly of different lengths) defined over an alphabet of m symbols. Some instances have been randomly generated and some other instances have been obtained from biological sequences.

Suicide bombing is an infamous form of terrorism that is becoming increasingly prevalent in the current era of global terror warfare. We consider the case of targeted attacks of this kind, and the use of detectors distributed over the area under threat as a protective countermeasure. Such detectors are non-fully reliable, and must be strategically placed in order to maximize the chances of detecting the attack, hence minimizing the expected number of casualties. To this end, different metaheuristic approaches based on local search and on population-based search (such as a hill climber, different Greedy randomized adaptive search procedures, an evolutionary algorithm and several estimation of distribution algorithms) are considered and benchmarked against a powerful greedy heuristic from the literature. We conduct an extensive empirical evaluation on synthetic instances featuring very diverse properties. Most metaheuristics outperform the greedy algorithm, and a hill-climber is shown to be superior to remaining approaches. This hill-climber is subsequently subject to a sensitivity analysis to determine which problem features make it stand above the greedy approach, and is finally deployed on a number of problem instances built after realistic scenarios, corroborating the good performance of the heuristic.

Memetic algorithms (MAs) constitute a search and optimization paradigm based on the orchestrated interplay between global and local search components, and have the exploitation of specific problem knowledge as one of their central tenets. MAs can take different forms although a classical incarnation involves the integration of independent search processes within a population-based optimization approach. We discuss this basic structure as well as several of the issues arising in the design process. This paves the way for providing a glimpse of some algorithmic extensions of this basic scheme. After providing an overview of the numerous practical applications of MAs, we close this article with some current perspectives of these techniques.

The Far From Most String Problem (FFMSP) is a string selection problem. The objective is to find a string whose distance to other strings in a certain input set is above a given threshold for as many of those strings as possible. This problem has links with some tasks in computational biology and its resolution has been shown to be very hard. We propose a memetic algorithm (MA) to tackle the FFMSP. This MA exploits a heuristic objective function for the problem and features initialization of the population via a Greedy Randomized Adaptive Search Procedure (GRASP) metaheuristic, intensive recombination via path relinking and local improvement via hill climbing. An extensive empirical evaluation using problem instances of both random and biological origin is done to assess parameter sensitivity and draw performance comparisons with other state-of-the-art techniques. The MA is shown to perform better than these latter techniques with statistical significance.