Results 271 to 280 of about 600,362 (302)
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Proceedings of the 10th annual conference on Genetic and evolutionary computation, 2008
While coevolution has many parallels to natural evolution, methods other than those based on evolutionary principles may be used in the interactive fitness setting. In this paper we present a generalization of coevolution to co-optimization which allows arbitrary black-box function optimization techniques to be used in a coevolutionary like manner.We ...
Travis C. Service, Daniel R. Tauritz
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While coevolution has many parallels to natural evolution, methods other than those based on evolutionary principles may be used in the interactive fitness setting. In this paper we present a generalization of coevolution to co-optimization which allows arbitrary black-box function optimization techniques to be used in a coevolutionary like manner.We ...
Travis C. Service, Daniel R. Tauritz
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Expert Systems with Applications, 2020
Abstract This paper proposes a novel metaheuristic algorithm called Chimp Optimization Algorithm (ChOA) inspired by the individual intelligence and sexual motivation of chimps in their group hunting, which is different from the other social predators.
Mohammad Khishe, Mohammad Reza Mosavi
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Abstract This paper proposes a novel metaheuristic algorithm called Chimp Optimization Algorithm (ChOA) inspired by the individual intelligence and sexual motivation of chimps in their group hunting, which is different from the other social predators.
Mohammad Khishe, Mohammad Reza Mosavi
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Optimization of algorithms with OPAL
Mathematical Programming Computation, 2014zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Charles Audet +2 more
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Fireworks Algorithm for Optimization
2010Inspired by observing fireworks explosion, a novel swarm intelligence algorithm, called Fireworks Algorithm (FA), is proposed for global optimization of complex functions In the proposed FA, two types of explosion (search) processes are employed, and the mechanisms for keeping diversity of sparks are also well designed In order to demonstrate the ...
Ying Tan 0002, Yuanchun Zhu
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The Algorithmics of Write Optimization
2018 IEEE International Parallel and Distributed Processing Symposium (IPDPS), 2018Write-optimized dictionaries (WODs), such as LSM trees and B^epsilon trees, are increasingly used in databases and file systems. Such data structures support very fast insertions without sacrificing lookup performance. This talk explains how WODs can substantially reduce the I/O cost of many workloads, enabling some applications to scale by orders of ...
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A Lexicographic Optimization Algorithm
Automation and Remote Control, 2004zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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A hybrid grasshopper optimization algorithm with bat algorithm for global optimization
Multimedia Tools and Applications, 2020This paper introduces a hybrid grasshopper optimization algorithm with bat algorithm (BGOA) for global optimization. In the BGOA, the Levy flight with variable coefficient is employed to enhance the exploration capability of the GOA. Then, the local search operation of bat algorithm (BA) is combined to balance the exploration and exploitation ...
Shenghan Yue, Hongbo Zhang
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Seasons optimization algorithm
Engineering with Computers, 2020This paper introduces a new stochastic bio-inspired optimization algorithm, denoted as seasons optimization (SO) algorithm. This algorithm is inspired by the growth cycle of trees in different seasons of a year. It is an iterative and population-based algorithm working with a population of initial solutions known as a forest.
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Virulence Optimization Algorithm
Applied Soft Computing, 2016A novel method of optimization inspired by optimal mechanism of viruses when infecting body cells.Simulates the recognition of fittest viruses, reproduction of cells to prompt "invasion" operation and escaping from infected region (to avoid immune reaction).It starts the optimization process with an initial population consisting of viruses and host ...
Morteza Jaderyan, Hassan Khotanlou
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2004
Assume we are trying to learn a concept class C of VC dimension d with respect to an arbitrary distribution. There is PAC sample size bound that holds for any algorithm that always predicts with some consistent concept in the class C (BEHW89): \(O(\frac{1}{\epsilon}(dlog\frac{1}{\epsilon}+log\frac{1}{\epsilon}))\), where e and δ are the accuracy and ...
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Assume we are trying to learn a concept class C of VC dimension d with respect to an arbitrary distribution. There is PAC sample size bound that holds for any algorithm that always predicts with some consistent concept in the class C (BEHW89): \(O(\frac{1}{\epsilon}(dlog\frac{1}{\epsilon}+log\frac{1}{\epsilon}))\), where e and δ are the accuracy and ...
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