Results 281 to 290 of about 356,284 (355)
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Annealed Differential Evolution
2007 IEEE Congress on Evolutionary Computation, 2007Differential evolution (DE) has recently emerged as a leading methodology for global search and optimization over continuous, high-dimensional spaces. It has been successfully applied to a wide variety of nearly intractable engineering problems. However, the DE and its variants usually employ a deterministic selection mechanism that always allows the ...
Swagatam Das +2 more
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MPPCEDE: Multi-population parallel co-evolutionary differential evolution for parameter optimization
, 2021In this paper, a novel multi-population parallel co-evolutionary differential evolution, named MPPCEDE, is proposed to optimize parameters of photovoltaic (PV) models and enhance conversion efficiency of solar energy. In the MPPCEDE, the reverse learning
Yingjie Song +6 more
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Novel binary differential evolution algorithm for knapsack problems
Information Sciences, 2021The capability of the conventional differential evolution algorithm to solve optimization problems in continuous spaces has been well demonstrated and documented in the literature. However, differential evolution has been commonly considered inapplicable
Ismail M. Ali +2 more
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Differentiable Kernel Evolution
2019 IEEE/CVF International Conference on Computer Vision (ICCV), 2019This paper proposes a differentiable kernel evolution (DKE) algorithm to find a better layer-operator for the convolutional neural network. Unlike most of the other neural architecture searching (NAS) technologies, we consider the searching space in a fundamental scope: kernel space, which encodes the assembly of basic multiply-accumulate (MAC ...
Yu Liu 0015 +3 more
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A differential evolution strategy
2017 IEEE Congress on Evolutionary Computation (CEC), 2017This contribution introduces an evolutionary algorithm (EA) for continuous optimization in ℝn. The algorithm generates new individuals by the standard nonelitist truncation selection and the differential mutation to generate new individuals. The differential mutation is enriched by adding a random vector in the direction of the shift of population ...
Dariusz Jagodzinski, Jaroslaw Arabas
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On the evolution of differentiation
Archiv f�r Mikrobiologie, 1967Evidence is presented that differentiating systems are closed off from their environment and self-sufficient; they must therefore operate on an endogenous metabolism. This fundamental characteristic of differentiation is exmained from the point of view of its evolution and with respect to the initiation, control and consumation of morphogenesis.
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Variable Fragments Evolution in Differential Evolution
2021The crossover operator plays an important role in Differential Evolution. However, the choice of proper crossover operator and corresponding parameters is dependent on the features of the problems. It is not easy for practitioners to choose the right crossover operator and associated parameter value.
Changshou Deng +3 more
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Fractional Order Differential Evolution
IEEE Transactions on Evolutionary ComputationDifferential evolution (DE) is a widely recognized method to solve complex optimization problems as shown by many researchers. Yet, nonadaptive versions of DE suffer from insufficient exploration ability and uses no historical information for its ...
Kaiyu Wang +4 more
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An Improved Quantum-Inspired Differential Evolution Algorithm for Deep Belief Network
IEEE Transactions on Instrumentation and Measurement, 2020Deep belief network (DBN) is one of the most representative deep learning models. However, it has a disadvantage that the network structure and parameters are basically determined by experiences. In this article, an improved quantum-inspired differential
Wu Deng +4 more
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Proceedings of the 15th annual conference companion on Genetic and evolutionary computation, 2013
Differential Evolution (DE) is one of the most powerful stochastic real-parameter optimization algorithms of current interest. DE operates through similar computational steps as employed by a standard Evolutionary Algorithm (EA). However, unlike traditional EAs, the DE-variants perturb the current-generation population members with the scaled ...
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Differential Evolution (DE) is one of the most powerful stochastic real-parameter optimization algorithms of current interest. DE operates through similar computational steps as employed by a standard Evolutionary Algorithm (EA). However, unlike traditional EAs, the DE-variants perturb the current-generation population members with the scaled ...
+4 more sources