Evolutionary programming made faster

By Xin Yao, Yong Liu, Guangming Lin. 1999

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Citations in the corpus

Convolution by Evolution: Differentiable Pattern Producing Networks (2016, methodology): “ally, 0.3, 0.5, and 0.5 respectively per replication event. We also experiment with applying Cauchy mutation to the copy of the winner after tness evaluation with a multiplicative co-ecient of 0.001 [31]. Cauchy mutation is preferred because most mutations are small, but because of its heavy tail, a”
Lévy-Flight Moth-Flame Algorithm for Function Optimization and Engineering Design Problems (2016, methodology): “Some popular algorithms in this field are Genetic Algorithms (GA) [1, 2], Particle Swarm Optimization (PSO) [3], Ant Colony Optimization (ACO) [4], Evolutionary Strategy (ES) [5], Differential Evolution (DE) [6], and Evolutionary Programming (EP) [7].”
Self-Adapting Control Parameters in Differential Evolution: A Comparative Study on Numerical Benchmark Problems (2006, methodology): “In [16], ten benchmark functions are used, and nine of them are the same as the benchmark functions in [23] paper.”
Adaptive Particle Swarm Optimization (2008, methodology): “For a thorough comparison, the t-test [53], [58] has also been carried out.”
Opposition-Based Differential Evolution (2008, unknown): “• Population size, [15], [33], [34].”
JADE: Adaptive Differential Evolution With Optional External Archive (2009, methodology): “In this section, JADE is applied to minimize a set of 13 scalable benchmark functions of dimensions D = 30 or 100 [29], [35] and a set of Dixon–Szegö functions of lower dimension D = 2 ∼ 6 [36], as shown in Tables II and III.”
Self-adaptive differential evolution with neighborhood search (2008, methodology): “To illustrate this problem, we conducted an experiment with SaNSDE+SaCR on the well-known Generalized Rosen-brock’s function [11].”
Evolutionary population dynamics and grey wolf optimizer (2014, background): “The selected test functions are presented in Tables 1 and 2 [51–55].”
A multiagent genetic algorithm for global numerical optimization (2004, methodology): “In [ 23 ], the termination criterion of FEP was to run 1500 generations for and – , 2000 generations for and , 5000 generations for and – , and 9000 generations for . In [8], the termination criterion of OGA/Q was the quality of the solution cannot be further improved in successive 50 generations af”
Wind Driven Optimization (WDO): A novel nature-inspired optimization algorithm and its application to electromagnetics (2010, methodology): “Numerical Study with Benchmark Functions In order to test the performance of the WDO algorithm, we experimented with five benchmark functions which are defined in [8].”
A new hybrid PSOGSA algorithm for function optimization (2010, methodology): “To evaluate the performance of PSOGSA, it is applied to 23 standard benchmark functions [12].”
Northern Goshawk Optimization: A New Swarm-Based Algorithm for Solving Optimization Problems (2021, methodology): “For this purpose, NGO is implemented on sixty-eight different objec-tive functions including unimodal, high-dimensional multi-modal, ﬁxed-dimensional multimodal [37], CEC2015 [38], and CEC2017 [39].”