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检索条件"主题词=Distributed evolutionary algorithms"

共 32 条记录，以下是21-30 订阅

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Unreliable Heterogeneous Workers in a Pool-Based evolutionary Algorithm 17

引用

17th European Conference on Applications of evolutionary Computation (EvpApplications)

作者： Garcia-Valdez, Mario Merelo Guervos, Juan Julian Fernandez de Vega, Francisco Inst Tecnol Tijuana Tijuana BC Mexico Univ Granada Granada Spain Univ Extremadura Grp Evoluc Artificial Merida Spain

ISBN: (纸本)9783662455234;9783662455227

In this paper the effect of node unavailability in algorithms using EvoSpace, a pool-based evolutionary algorithm, is assessed. EvoSpace is a framework for developing evolutionary algorithms (EAs) using heterogeneous and unreliable resources. It is based on Linda's tuple space coordination model. The core elements of EvoSpace are a central repository for the evolving population and remote clients, here called EvoWorkers, which pull random samples of the population to perform on them the basic evolutionary processes (selection, variation and survival), once the work is done, the modified sample is pushed back to the central population. To address the problem of unreliable EvoWorkers, EvoSpace uses a simple re-insertion algorithm using copies of samples stored in a global queue which also prevents the starvation of the population pool. Using a benchmark problem from the P-Peaks problem generator we have compared two approaches: (i) the re-insertion of previous individuals at the cost of keeping copies of each sample, and a common approach of other pool based EAs, (ii) inserting randomly generated individuals. We found that EvoSpace is fault tolerant to highly unreliable resources and also that the re-insertion algorithm is only needed when the population is near the point of starvation.

关键词： distributed evolutionary algorithms Cloud computing

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Addressing High Dimensional Multi-objective Optimization Problems by Coevolutionary Islands with Overlapping Search Spaces 19th

Addressing High Dimensional Multi-objective Optimization Pro...

引用

19th European Conference on the Applications of evolutionary Computation (EvoApplications)

作者： Garcia-Sanchez, Pablo Ortega, Julio Gonzalez, Jesus Castillo, Pedro A. Merelo, Juan J. Univ Granada Dept Comp Architecture & Technol Granada Spain

ISBN: (纸本)9783319311531

Large-scale multi-objective optimization problems with many decision variables have recently attracted the attention of researchers as many data mining applications involving high dimensional patterns can be leveraged using them. Current parallel and distributed computer architectures can provide the required computing capabilities to cope with these problems once efficient procedures are available. In this paper we propose a cooperative coevolutionary island-model procedure based on the parallel execution of sub-populations, whose individuals explore different domains of the decision variables space. More specifically, the individuals belonging to the same sub-population (island) explore the same subset of decision variables. Two alternatives to distribute the decision variables among the different sub-populations have been considered and compared here. In the first approach, individuals in different sub-population explore disjoint subsets of decision variables (i.e. the chromosomes are divided into disjoints subsets). Otherwise, in the second alternative there are some overlapping among the variables explored by individuals in different sub-populations. The analysis of the obtained experimental results, by using different metrics, shows that coevolutionary approaches provide statistically significant improvements with respect to the base algorithm, being the relation of the number of islands (subpopulations) to the length of the chromosome (number of decision variables) a relevant factor to determine the most efficient alternative to distribute the decision variables.

关键词： Multi-objective algorithms NSGA-II Island model distributed evolutionary algorithms

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Solving Combinatorial Puzzles with Parallel evolutionary algorithms 12th

Solving Combinatorial Puzzles with Parallel Evolutionary Alg...

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12th International Conference on Large-Scale Scientific Computations (LSSC)

作者： Balabanov, Todor Ivanov, Stoyan Ketipov, Rumen Bulgarian Acad Sci Inst Informat & Commun Technol Acad Georgi Bonchev StrBlock 2 Sofia 1113 Bulgaria

ISBN: (纸本)9783030410322;9783030410315

Rubik's cube is the most popular combinatorial puzzle. It is well known that solutions of the combinatorial problems are generally hard to find. If 90. clockwise rotations of the cube's sides are taken as operations it will give a minimal cube's grammar. By building formal grammar sentences with the usage of the six operations ([L]eft, [R]ight, [T]op, [D]own, [F]ront, [B]ack) all cube's permutations can be achieved. In an evolutionary algorithms (like genetic algorithms for example) set of formal grammar sentences can be represented as population individuals. Single cut point crossover can be efficiently applied when population individuals are strings. Changing randomly selected operation with another randomly selected operation can be used as efficient mutation operator. The most important part of such global optimization is the fitness function. For better individuals fitness value evaluation a combination between Euclidean and Hausdorff distances is proposed in this research. The experiments in this research are done as parallel program written in C++ and Open MPI.

关键词： distributed evolutionary algorithms Combinatorial puzzles Integer optimization

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Random Selection of Parameters in Asynchronous Pool-Based evolutionary algorithms

Random Selection of Parameters in Asynchronous Pool-Based Ev...

引用

IEEE Congress on evolutionary Computation (IEEE CEC)

作者： Garcia-Valdez, Mario Marquez, Rene Trujillo, Leonardo Merelo, J. J. Inst Tecnol Tijuana Tijuana BC Mexico Univ Granada Comp Architecture & Technol Granada Spain

ISBN: (纸本)9781728183923

Synchronous operation is not the most natural, as in biologically inspired, mode to run distributed algorithms. In many grid, cloud or volunteer setups nodes are heterogeneous, or simply are not available at the exact same time;this is a challenge for the researcher if their full performance is going to be actually leveraged. Asynchronous distributed evolutionary algorithms try to solve this by dropping the homogeneity, as well as the synchronicity, assumption. These algorithms share the population between distributed workers which execute the actual evolutionary process by taking samples of the population, and replacing them in the population pool by evolved individuals. The performance of these EAs depends in part on the selection of parameters for the EA running in each worker. In this paper we study how randomly varying parameters in distributed evolutionary algorithms affects performance. Experiments were conducted in the AWS cloud using 2, 6 and 12 virtual machine configurations, with both homogeneous and heterogeneous random settings using five test functions for real-valued optimization and the OneMax binary problem. The results suggest that this method can produce a performance that is competitive with instances of the algorithm using workers with parameters specially tuned for the benchmark.

关键词： distributed evolutionary algorithms Volunteer Computing Cloud Computing pool-based evolutionary algorithms parameter optimization

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Increasing Performance via Gamification in a Volunteer-Based evolutionary Computation System 17th

Increasing Performance via Gamification in a Volunteer-Based...

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17th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU)

作者： Garcia-Valdez, Mario Merelo Guervos, Juan Julian Lara, Lucero Garcia-Sanchez, Pablo Inst Tecnol Tijuana Tijuana BC Mexico Univ Granada Granada Spain Univ Cadiz Cadiz Spain

ISBN: (纸本)9783319914794;9783319914787

distributed computing systems can be created using volunteers, users who spontaneously, after receiving an invitation, decide to provide their own resources or storage to contribute to a common effort. They can, for instance, run a script embedded in a web page;thus, collaboration is straightforward, but also ephemeral, with resources depending on the amount of time the user decides to lend. This implies that the user has to be kept engaged so as to obtain as many computing cycles as possible. In this paper, we analyze a volunteer-based evolutionary computing system called NodIO with the objective of discovering design decisions that encourage volunteer participation, thus increasing the overall computing power. We present the results of an experiment in which a gamification technique is applied by adding a leader-board showing the top scores achieved by registered contributors. In NodIO, volunteers can participate without creating an account, so one of the questions we wanted to address was if the need to register would have a negative impact on user participation. The experiment results show that even if only a small percentage of users created an account, those participating in the competition provided around 90% of the work, thus effectively increasing the performance of the overall system.

关键词： distributed evolutionary algorithms Volunteer computing Socio-technical systems

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evospace-js: Asynchronous Pool-Based Execution of Heterogeneous Metaheuristics 17

evospace-js: Asynchronous Pool-Based Execution of Heterogene...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Garcia-Valdez, Mario Merelo, J. J. Inst Tecnolg Tijuana Tijuana Bc Mexico Univ Granada Dept ATC CITIC Grp GeNeura Granada Spain

ISBN: (纸本)9781450349390

This paper is part of a continuing effort in the field of EC to develop algorithms that follow an opportunistic approach to computing, allowing the exploitation of freely available services over the Internet by using free tiers of cloud services or volunteer computing resources;the EvoSpace model is able to tap from both kind of resources, using asynchronous evolutionary algorithms. We present its design, which follows an an event-driven architecture and asynchronous I/O model, and its implementation, with a server-side tier programmed in *** that uses Redis as an in-memory and high performance data store for the population. This population store is exposed to clients running population-based and nature-inspired metaheuristics through a REST API. Additional capabilities where implemented in this version to allow the logging of experiments where heterogeneous algorithms are executed in parallel. These logs can then be transformed to other formats. As a case study an hybrid global optimization algorithm has been implemented mixing two algorithms: a PSO algorithm from the EvoloPy library and a GA using the DEAP framework. The result was transformed to files compatible to the Comparing Continuous Optimizer platform in order to use their post-processing code. Clients in this case have been developed in the Python language, the language used to implement both libraries. The results from this case study suggest, first, that EvoSpace can be used as a paradigm-and language-agnostic platform for population-based optimization algorithms, and also that this software can yield performance improvements and a viable platform to execute and compare asynchronous pool-based metaheuristics.

关键词： Nature-inspired metaheuristics distributed evolutionary algorithms

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Benchmarking a Pool-Based Execution with GA and PSO Workers on the BBOB Noiseless Testbed 17

Benchmarking a Pool-Based Execution with GA and PSO Workers ...

引用

Genetic and evolutionary Computation Conference (GECCO)

作者： Garcia-Valdez, Mario Merelo, J. J. Inst Tecnolg Tijuana Tijuana Bc Mexico Univ Granada Dept ATC CITIC Grp GeNeura Granada Spain

ISBN: (纸本)9781450349390

In this work, we evaluate an asynchronous population-based algorithm following a pool-based approach. In Pool-based algorithms, a collection of workers collaborates through a shared population repository. In particular, we followed the EvoSpace approach in which workers asynchronously interact with a population pool by taking samples of the population to perform a standard search on the samples, to then return newly evolved solutions back to the pool. For this purpose, we use the BBOB Noiseless Testbed and a hybrid algorithm combining two kinds or workers: PSO and GA. We find that a Pool-based approach outperforms the canonical GA and PSO algorithms in nearly all cases. The results of these tests suggest that a Pool Based approach can be used to implement hybrid algorithms that can improve the performance of canonical population-based optimization algorithms.

关键词： Benchmarking Black-box optimization Nature-inspired metaheuristics distributed evolutionary algorithms

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Large Scale Evolution of Convolutional Neural Networks Using Volunteer Computing 17

Large Scale Evolution of Convolutional Neural Networks Using...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Desell, Travis Univ North Dakota 3950 Campus Rd Grand Forks ND 58201 USA

ISBN: (纸本)9781450349390

This work presents a new algorithm called evolutionary exploration of augmenting convolutional topologies (EXACT), which is capable of evolving the structure of convolutional neural networks (CNNs). EXACT is in part modeled after the neuroevolution of augmenting topologies (NEAT) algorithm, with notable exceptions to allow it to scale to large scale distributed computing environments and evolve networks with convolutional filters. In addition to multi-threaded and MPI versions, EXACT has been implemented as part of a BOINC volunteer computing project, allowing large scale evolution. During a period of two months, over 4,500 volunteered computers on the Citizen Science Grid trained over 120,000 CNNs and evolved networks reaching 98.32% test data accuracy on the MNIST handwritten digits dataset. Oese results are even stronger as the backpropagation strategy used to train the CNNs was fairly rudimentary (ReLU units, L2 regularization and Nesterov momentum) and these were initial test runs done without refinement of the backpropagation hyperparameters. Further, the EXACT evolutionary strategy is independent of the method used to train the CNNs, so they could be further improved by advanced techniques like elastic distortions, pretraining and dropout. The evolved networks are also quite interesting, showing "organic" structures and significant differences from standard human designed architectures.

关键词： Neuroevolution Convolutional Neural Networks Image Classification distributed evolutionary algorithms

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Heterogenní ostrovní modely

Heterogenní ostrovní modely

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作者： Balcar, Štěpán Charles University of Prague

The work deals with heterogeneous island models. The work designs and implements a new island model based on knowledge of homogeneous models of evolutionary algorithms. The model allows dynamic replanning of general c... 详细信息

关键词： distributed evolutionary algorithms adaptability island architecture

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Heterogenní ostrovní modely

Heterogenní ostrovní modely

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作者： Balcar, Štěpán Charles University of Prague

关键词： distributed evolutionary algorithms adaptability island architecture

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