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Titlebook: Bioinspired Optimization Methods and Their Applications; 10th International C Marjan Mernik,Tome Eftimov,Matej ?repin?ek Conference proceed

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發(fā)表于 2025-3-30 08:28:03 | 只看該作者
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發(fā)表于 2025-3-30 13:24:35 | 只看該作者
,Genetic Improvement of?TCP Congestion Avoidance,ernet. Its performance heavily relies on the management of the congestion window, which regulates the amount of packets that can be transmitted on the network. In this paper, we employ Genetic Programming (GP) for evolving novel congestion policies, encoded as C++ programs. We optimize the function
53#
發(fā)表于 2025-3-30 16:46:47 | 只看該作者
,Hybrid Acquisition Processes in?Surrogate-Based Optimization. Application to?Covid-19 Contact Reduce computationally expensive optimization problems. Differently, Parallel Surrogate-Driven Algorithms (P-SDAs) rely on the optimization of a surrogate-informed metric of promisingness to acquire new solutions. The former are promoted to deal with moderately computationally expensive problems while th
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發(fā)表于 2025-3-31 00:18:00 | 只看該作者
,Investigating the?Impact of?Independent Rule Fitnesses in?a?Learning Classifier System,tly proposed a new rule-based learning system, SupRB, to construct compact, interpretable and transparent models by utilizing separate optimizers for the model selection tasks concerning rule discovery and rule set composition. This allows users to specifically tailor their model structure to fulfil
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發(fā)表于 2025-3-31 01:32:45 | 只看該作者
Modified Football Game Algorithm for Multimodal Optimization of Test Task Scheduling Problems Using industries where the reliability of the final product is fundamentally dependent on those tests while the time, workload, and agility of the production is dependent on the optimal scheduling. Scheduling problems are highly multimodal problems with high number of local and global optimum solutions.
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發(fā)表于 2025-3-31 06:19:19 | 只看該作者
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發(fā)表于 2025-3-31 11:22:12 | 只看該作者
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發(fā)表于 2025-3-31 16:10:51 | 只看該作者
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發(fā)表于 2025-3-31 20:31:35 | 只看該作者
,SMOTE Inspired Extension for?Differential Evolution,regular basis attempting to ever more improve its performance. Typical avenues for improvement include the introduction of new (mutation) operators or parameter control schemes. Another, less common approach, is the incorporation of additional, complementary, search mechanisms. This paper proposes o
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