Approach for solving multimodal problems using Genetic Algorithms with Grouped into Species optimized with Predator-Prey

Over recent years, Genetic Algorithms have proven to be an appropriate tool for solving certain problems. However, it does not matter if the search space has several valid solutions, as their classic approach is insufficient. To this end, the idea of dividing the individuals into species has been successfully raised. However, this solution is not free of drawbacks, such as the emergence of redundant species, overlapping or performance degradation by significantly increasing the number of individuals to be evaluated. This paper presents the implementation of a method based on the predator-prey technique, with the aim of providing a solution to the problem, as well as a number of examples to prove its effectiveness

Application of Multiobjective Evolutionary Techniques for Robust Portfolio Optimization

On December 20 of 2012 Sandra García Rodríguez defended his PhD at Carlos III of Madrid (Spain), called: “Application of Multiobjective Techniques for Robust Portfolio Optimization”. This thesis was supervised by Dr. David Quintana Montero and Dr. Inés M. Galván León. The defense was done in a publicly open presentation held at Carlos III University of Madrid. The PhD was approved, with the highest rating Cum Laude, by the examining committee: Dr. José Manuel Molina López, Dr. Antonio Gaspar Lopes da Cunha and Dr. David Camacho Fernández

GPGPU Implementation of a Genetic Algorithm for Stereo Refinement

During the last decade, the general-purpose computing on graphics processing units Graphics (GPGPU) has turned out to be a useful tool for speeding up many scientific calculations. Computer vision is known to be one of the fields with more penetration of these new techniques. This paper explores the advantages of using GPGPU implementation to speedup a genetic algorithm used for stereo refinement. The main contribution of this paper is analyzing which genetic operators take advantage of a parallel approach and the description of an efficient state- of-the-art implementation for each one. As a result, speed-ups close to x80 can be achieved, demonstrating to be the only way of achieving close to real-time performance

Approach for solving multimodal problems using Genetic Algorithms with Grouped into Species optimized with Predator-Prey

Over recent years, Genetic Algorithms have proven to be an appropriate tool for solving certain problems. However, it does not matter if the search space has several valid solutions, as their classic approach is insufficient. To this end, the idea of dividing the individuals into species has been successfully raised. However, this solution is not free of drawbacks, such as the emergence of redundant species, overlapping or performance degradation by significantly increasing the number of individuals to be evaluated. This paper presents the implementation of a method based on the predator-prey technique, with the aim of providing a solution to the problem, as well as a number of examples to prove its effectiveness

Approach for solving multimodal problems using genetic algorithms with grouped into species optimized with predator-prey

Over recent years, Genetic Algorithms have proven to be an appropriate tool for solving certain problems. However, it does not matter if the search space has several valid solutions, as their classic approach is insufficient. To this end, the idea of dividing the individuals into species has been successfully raised. However, this solution is not free of drawbacks, such as the emergence of redundant species, overlapping or performance degradation by significantly increasing the number of individuals to be evaluated. This paper presents the implementation of a method based on the predator-prey technique, with the aim of providing a solution to the problem, as well as a number of examples to prove its effectiveness