By reading, you can know the knowledge and things more, not only about what you get from people t... more By reading, you can know the knowledge and things more, not only about what you get from people to people. Book will be more trusted. As this applications of evolutionary computation 18th european conference evoapplications 2015 copenhagen denmark april 8 1
Color mememaps of self-balancing strategies in an island-based selecto-Lamarckian model. This fig... more Color mememaps of self-balancing strategies in an island-based selecto-Lamarckian model. This figure is a companion to paper "Studying Self-Balancing Strategies in Island-Based Multimemetic Algorithms".
We consider the use of island-based evolutionary algorithms (EAs) on fault-prone computational se... more We consider the use of island-based evolutionary algorithms (EAs) on fault-prone computational settings. More precisely, we consider scenarios plagued with correlated node failures. To this end, we use the sandpile model in order to induce such complex, correlated failures in the system. Several EA variants featuring self-adaptive capabilities aimed to alleviate the impact of node failures are considered, and their performance is studied in both correlated and non-correlated scenarios for increasingly large volatility rates. Simple island-based EAs are shown to have a significant performance degradation in the correlated scenario with respect to its uncorrelated counterpart. Resilience is however much improved via the use of self-? properties (self-scaling and self-healing), which leads to a more gentle degradation profile. The inclusion of self-generation also contributes to boost performance, leading to negligible degradation in the scenarios considered.
The International Journal of High Performance Computing Applications, 2016
Computational environments emerging from the pervasiveness of networked devices offer a plethora ... more Computational environments emerging from the pervasiveness of networked devices offer a plethora of opportunities and challenges. The latter arise from their dynamic, inherently volatile nature that tests the resilience of algorithms running on them. Here we consider the deployment of population-based optimization algorithms on such environments, using the island model of memetic algorithms for this purpose. These memetic algorithms are endowed with self-★ properties that give them the ability to work autonomously in order to optimize their performance and to react to the instability of computational resources. The main focus of this work is analyzing the performance of these memetic algorithms when the underlying computational substrate is not only volatile but also heterogeneous in terms of the computational power of each of its constituent nodes. To this end, we use a simulated environment that allows experimenting with different volatility rates and heterogeneity scenarios (that...
Game design is a fundamental and critical part of the videogame development process, demanding a ... more Game design is a fundamental and critical part of the videogame development process, demanding a high cost in terms of time and effort from the team of designers. The availability of tools for assisting in this task is therefore of the foremost interest. These can not just speed up the process and reduce costs, but also improve the overall quality of the results by providing useful suggestions and hints. A conceptual system to approach the construction of this kind of tools is presented in this work. By using a learning component, the preferences and expertise of the designers can be modelled and to some extent simulated. This model is subsequently exploited by an optimization component that tries to create adequate game designs. A proof of concept of the system is provided in the context of level design in Metroidvania games. It is shown that the system can produce quality solutions and hints to the designer.
espanolEste trabajo estudia el remuestreo en algoritmos evolutivos aplicados a problemas de optim... more espanolEste trabajo estudia el remuestreo en algoritmos evolutivos aplicados a problemas de optimizacion combinatoria. Para ello se han escogido tres problemas diferentes: la construccion de la base de reglas de un controlador borroso, la planificacion de un sistema de produccion y la optimizacion de una funcion matematica. Los resultados obtenidos demuestran que las distintas tecnicas tienen un comportamiento coherente en dichos problemas, y que el remuestreo se reduce a medida que aumenta el tamano de la representacion. Adicionalmente, se muestra como el uso de un registro de la evolucion de los algoritmos puede reducir notablemente su tiempo de ejecucion cuando la funcion de evaluacion es costosa. EnglishThis work studies the retracing properties of evolutionary algorithms applied to combinatorial optimisation problems. For that purpose, three different problems have been chosen: the design of a fuzzy-controller rule-base, a flowshop scheduling and numerical optimisation. The obt...
Adaptive Tabu Tenure Computation in Local Search.- A Conflict Tabu Search Evolutionary Algorithm ... more Adaptive Tabu Tenure Computation in Local Search.- A Conflict Tabu Search Evolutionary Algorithm for Solving Constraint Satisfaction Problems.- Cooperative Particle Swarm Optimization for the Delay Constrained Least Cost Path Problem.- Effective Neighborhood Structures for the Generalized Traveling Salesman Problem.- Efficient Local Search Limitation Strategies for Vehicle Routing Problems.- Evolutionary Local Search for the Minimum Energy Broadcast Problem.- Exploring Multi-objective PSO and GRASP-PR for Rule Induction.- An Extended Beam-ACO Approach to the Time and Space Constrained Simple Assembly Line Balancing Problem.- Graph Colouring Heuristics Guided by Higher Order Graph Properties.- A Hybrid Column Generation Approach for the Berth Allocation Problem.- Hybrid Metaheuristic for the Prize Collecting Travelling Salesman Problem.- An ILS Based Heuristic for the Vehicle Routing Problem with Simultaneous Pickup and Delivery and Time Limit.- An Immune Genetic Algorithm Based on Bottleneck Jobs for the Job Shop Scheduling Problem.- Improved Construction Heuristics and Iterated Local Search for the Routing and Wavelength Assignment Problem.- Improving Metaheuristic Performance by Evolving a Variable Fitness Function.- Improving Query Expansion with Stemming Terms: A New Genetic Algorithm Approach.- Inc*: An Incremental Approach for Improving Local Search Heuristics.- Metaheuristics for the Bi-objective Ring Star Problem.- Multiobjective Prototype Optimization with Evolved Improvement Steps.- Optimising Multiple Kernels for SVM by Genetic Programming.- Optimization of Menu Layouts by Means of Genetic Algorithms.- A Path Relinking Approach with an Adaptive Mechanism to Control Parameters for the Vehicle Routing Problem with Time Windows.- Reactive Stochastic Local Search Algorithms for the Genomic Median Problem.- Solving Graph Coloring Problems Using Learning Automata.
By reading, you can know the knowledge and things more, not only about what you get from people t... more By reading, you can know the knowledge and things more, not only about what you get from people to people. Book will be more trusted. As this applications of evolutionary computation 18th european conference evoapplications 2015 copenhagen denmark april 8 1
Color mememaps of self-balancing strategies in an island-based selecto-Lamarckian model. This fig... more Color mememaps of self-balancing strategies in an island-based selecto-Lamarckian model. This figure is a companion to paper "Studying Self-Balancing Strategies in Island-Based Multimemetic Algorithms".
We consider the use of island-based evolutionary algorithms (EAs) on fault-prone computational se... more We consider the use of island-based evolutionary algorithms (EAs) on fault-prone computational settings. More precisely, we consider scenarios plagued with correlated node failures. To this end, we use the sandpile model in order to induce such complex, correlated failures in the system. Several EA variants featuring self-adaptive capabilities aimed to alleviate the impact of node failures are considered, and their performance is studied in both correlated and non-correlated scenarios for increasingly large volatility rates. Simple island-based EAs are shown to have a significant performance degradation in the correlated scenario with respect to its uncorrelated counterpart. Resilience is however much improved via the use of self-? properties (self-scaling and self-healing), which leads to a more gentle degradation profile. The inclusion of self-generation also contributes to boost performance, leading to negligible degradation in the scenarios considered.
The International Journal of High Performance Computing Applications, 2016
Computational environments emerging from the pervasiveness of networked devices offer a plethora ... more Computational environments emerging from the pervasiveness of networked devices offer a plethora of opportunities and challenges. The latter arise from their dynamic, inherently volatile nature that tests the resilience of algorithms running on them. Here we consider the deployment of population-based optimization algorithms on such environments, using the island model of memetic algorithms for this purpose. These memetic algorithms are endowed with self-★ properties that give them the ability to work autonomously in order to optimize their performance and to react to the instability of computational resources. The main focus of this work is analyzing the performance of these memetic algorithms when the underlying computational substrate is not only volatile but also heterogeneous in terms of the computational power of each of its constituent nodes. To this end, we use a simulated environment that allows experimenting with different volatility rates and heterogeneity scenarios (that...
Game design is a fundamental and critical part of the videogame development process, demanding a ... more Game design is a fundamental and critical part of the videogame development process, demanding a high cost in terms of time and effort from the team of designers. The availability of tools for assisting in this task is therefore of the foremost interest. These can not just speed up the process and reduce costs, but also improve the overall quality of the results by providing useful suggestions and hints. A conceptual system to approach the construction of this kind of tools is presented in this work. By using a learning component, the preferences and expertise of the designers can be modelled and to some extent simulated. This model is subsequently exploited by an optimization component that tries to create adequate game designs. A proof of concept of the system is provided in the context of level design in Metroidvania games. It is shown that the system can produce quality solutions and hints to the designer.
espanolEste trabajo estudia el remuestreo en algoritmos evolutivos aplicados a problemas de optim... more espanolEste trabajo estudia el remuestreo en algoritmos evolutivos aplicados a problemas de optimizacion combinatoria. Para ello se han escogido tres problemas diferentes: la construccion de la base de reglas de un controlador borroso, la planificacion de un sistema de produccion y la optimizacion de una funcion matematica. Los resultados obtenidos demuestran que las distintas tecnicas tienen un comportamiento coherente en dichos problemas, y que el remuestreo se reduce a medida que aumenta el tamano de la representacion. Adicionalmente, se muestra como el uso de un registro de la evolucion de los algoritmos puede reducir notablemente su tiempo de ejecucion cuando la funcion de evaluacion es costosa. EnglishThis work studies the retracing properties of evolutionary algorithms applied to combinatorial optimisation problems. For that purpose, three different problems have been chosen: the design of a fuzzy-controller rule-base, a flowshop scheduling and numerical optimisation. The obt...
Adaptive Tabu Tenure Computation in Local Search.- A Conflict Tabu Search Evolutionary Algorithm ... more Adaptive Tabu Tenure Computation in Local Search.- A Conflict Tabu Search Evolutionary Algorithm for Solving Constraint Satisfaction Problems.- Cooperative Particle Swarm Optimization for the Delay Constrained Least Cost Path Problem.- Effective Neighborhood Structures for the Generalized Traveling Salesman Problem.- Efficient Local Search Limitation Strategies for Vehicle Routing Problems.- Evolutionary Local Search for the Minimum Energy Broadcast Problem.- Exploring Multi-objective PSO and GRASP-PR for Rule Induction.- An Extended Beam-ACO Approach to the Time and Space Constrained Simple Assembly Line Balancing Problem.- Graph Colouring Heuristics Guided by Higher Order Graph Properties.- A Hybrid Column Generation Approach for the Berth Allocation Problem.- Hybrid Metaheuristic for the Prize Collecting Travelling Salesman Problem.- An ILS Based Heuristic for the Vehicle Routing Problem with Simultaneous Pickup and Delivery and Time Limit.- An Immune Genetic Algorithm Based on Bottleneck Jobs for the Job Shop Scheduling Problem.- Improved Construction Heuristics and Iterated Local Search for the Routing and Wavelength Assignment Problem.- Improving Metaheuristic Performance by Evolving a Variable Fitness Function.- Improving Query Expansion with Stemming Terms: A New Genetic Algorithm Approach.- Inc*: An Incremental Approach for Improving Local Search Heuristics.- Metaheuristics for the Bi-objective Ring Star Problem.- Multiobjective Prototype Optimization with Evolved Improvement Steps.- Optimising Multiple Kernels for SVM by Genetic Programming.- Optimization of Menu Layouts by Means of Genetic Algorithms.- A Path Relinking Approach with an Adaptive Mechanism to Control Parameters for the Vehicle Routing Problem with Time Windows.- Reactive Stochastic Local Search Algorithms for the Genomic Median Problem.- Solving Graph Coloring Problems Using Learning Automata.
Uploads
Papers by Carlos Cotta