The Differential Evolution (DE) algorithm is a kind of evolutionary algorithm, which was first proposed by Storn and Price in 1995[47]. DE and other evolutionary algorithms common ground is that they are all a population-based algorithms covering the procedure below: crossover, mutation and selec...
Differential Evolution Algorithm (DEA) is a simple population-based, stochastic parallel search evolutionary algorithm for global optimization and is capable of handling non-differentiable, nonlinear and multi-modal objective functions. In DEA the population consists of real-valued vectors with dimension D...
Differential Evolution Algorithm Differential Evolution Strategy Differential Expression Using Customized Amplification Library Differential extraction Differential Fair Buffer Allocation differential fault Differential Fault Analysis differential field diagnosis differential fill-up collar differential flotation Differential ...
This paper introduces an Effective Differential Evolution (EDE) algorithm for solving real parameter optimization problems over continuous domain. The proposed algorithm proposes a new mutation rule based on the best and the worst individuals among the entire population of a particular generation. The mu...
To enhance the performance of DL models, a novel pruning DE-DL method is proposed, which employs the differential evolution (DE) algorithm to optimize architecture and continuous and discrete-valued hyper-parameters. The proposed DE-DL method is so generic that it can be applied to optimize ...
DE/EDA: a new evolutionary algorithm for global optimization Inf. Sci. (2005) Y. Wang et al. Utilizing cumulative population distribution information in differential evolution Appl. Soft Comput. (2016) Y. Zhou et al. Differential evolution with guiding archive for global numerical optimization Appl...
2.1. Differential evolution Recently, differential evolution has gained much importance because of its efficacy in solving real parameter optimization problems (Das and Suganthan, 2010). It is a powerful population based stochastic optimization algorithm with three operators which are: 2.1.1. Mutation In...
regression (Efron et al., 2004), least absolute shrinkage and selection operator (Tibshirani, 1996), random forests (RFs) (Breiman, 2001), traditional genetic programming (GP-Trad) (Koza, 1994), and genetic programming—gene pool optimal mixing evolutionary algorithm (Virgolin et al., 2017)....
The QSIM algorithm (Kuipers, 1986, 1994) performs qualitative simulation by deriving the immediate successors of each qualitative state, repeating this step to grow the behavior graph from the initial state at its root. In order to guarantee that all possible behaviors are predicted, we require fi...
select article A weighted numerical algorithm for two and three dimensional two-sided space fractional wave equations Research articleAbstract only A weighted numerical algorithm for two and three dimensional two-sided space fractional wave equations ...