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Showing 1 to 8 of 8 for “"distribution algorithms"”.
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Effective and efficient estimation of distribution algorithms for permutation and scheduling problems.
Estimation of Distribution Algorithm (EDA) is a branch of evolutionary computation that learn a probabilistic model of good solutions. Probabilistic models are used to represent relationships between solution variables which may give useful, human-understandable insights into real-world problems. …
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Hibridinių paskirstytųjų skaičiavimų dalijimosi platforma /
… in this dissertation uses the created task distribution method for tasks distribution between clusters. Although this method is not new, it has not yet been applied to hybrid distributed computing platforms. The proposed method uses a task stalling buffer, thus avoiding straggling tasks in …
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Networking Requirements and Solutions for a TV WWW Browser
Most people cannot access the World Wide Web (WWW) and other Internet services because access requires a complex and expensive computer. Moreover, the bandwidth offered to the general public is mostly limited by today's analog modems through standard telephone lines. An inexpensive, easy-to-use …
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DEUM: a framework for an estimation of distribution algorithm based on Markov random fields.
Estimation of Distribution Algorithms (EDAs) belong to the class of population based optimisation algorithms. They are motivated by the idea of discovering and exploiting the interaction between variables in the solution. They estimate a probability distribution from population of solutions, and …
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Incorporating Memory and Learning Mechanisms Into Meta-RaPS
… The first approach taken is Estimation of Distribution Algorithms (EDA), a stochastic learning technique that creates a probability distribution for each decision variable to generate new solutions. The second Meta-RaPS version was developed by utilizing a machine learning algorithm, Q …
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Adaptive scaling of evolvable systems
… A particular feature of models such as Genetic Algorithms (GA) [18, 12] is the incremental combination of partial solutions distributed within a population of solutions. This mechanism in principle allows certain problems to be solved which would not be amenable to a simple local search. Such …
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Advances in Hybrid Evolutionary Computation for Continuous Optimization
Evolutionary Algorithms (EAs) are a set of optimization techniques that have become highly popular in recent decades. One of the main reasons for this success is that they provide a general purpose mechanism for solving a wide range of problems. Several approaches have been proposed, each of them …
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Improving Automated Android Test Generation
… well as fitness evaluations. Since search-based algorithms require a substantial number of test executions to play out their strengths, the slow test execution impedes the effectiveness of the search. Third, the guidance offered by search-based algorithms is often hampered by applying inadequate …