Rahnamayan S,Wang G G.Center-based sampling for population-based algorithms[A].IEEE Congress on Evolutionary Computation[C].New York:IEEE,2009.933-938.S. Rahnamayan and G. G. Wang, "Center-based sampling for population-based algorithms," in Evolutionary Computation, 2009. CEC'09. IEEE ...
This paper discusses the relationship between data science and population-based algorithms, which include swarm intelligence and evolutionary algorithms. We reviewed two categories of literature, which include population-based algorithms solving data ana
Acasestudyofinnovativepopulation-basedalgorithmsin3Dmodeling: Artificialbeecolony,biogeography-basedoptimization,harmony search q JoséM.García-Torres a,⇑ ,SergioDamas b ,OscarCordón a,b,c ,JoséSantamaría d a Dept.ofComputerScienceandArtificialIntelligence,UniversityofGranada,Spain b EuropeanCent...
These results show that population-based algorithms can outperform traditional local search strategies. Besides, as far as we know, this is the first time that the method of musical composition is used for this kind of problems.doi:10.1108/K-06-2016-0130Rincón-García, Eric Alfredo...
The three algorithms were able to successfully solve the posed problem. However, differential evolution outperformed its counterparts both in quality of the obtained solutions and efficiency of search.doi:10.1007/s00500-013-1043-5Gabriela Ochoa
For such algorithms to be successful, at least three properties are required: (i) an effective informed sampling strategy, that guides the generation of new candidates on the basis of the fitnesses and locations of previously visited candidates; (ii) mechanisms to ensure efficiency, so that (...
Although a wide range of population-based algorithms, such as evolutionary algorithms, particle swarm optimizers, and differential evolution, have been developed and studied under this scenario, the performance of an algorithm may vary significantly from problem to problem. This implies that there is ...
Evolutionary algorithms (EAs) are population-based general-purpose optimization algorithms, and have been successfully applied in various real-world optimization tasks. However, previous theoretical studies often employ EAs with only a parent or offspring population and focus on specific problems. Further...
Genetic algorithms, while showing promise in exploring the search space, often struggle with premature convergence [8,9]. Traditional hill climbing methods [10,11], though computationally efficient, frequently become trapped in local optima, failing to achieve the desired nonlinearity. This paper ...
Evolutionary algorithms EC: Evolutionary computation FACO: Feature ACO FFNN: Feed-forward ANN FL: Fuzzy logic GAs: Genetic algorithms GARCH: Generalized autoregressive conditional heteroscedasticity GR(.): Griewank function HIAO: Hybridization of AIS and ACO approaches optimization IC: Informa...