A taxonomy for the crossover operator for real-coded genetic algorithms: An experimental study

Herrera, F.; Lozano, Manuel; Sánchez, Ana

doi:10.1002/int.10091

Cited by 339 publications

(199 citation statements)

References 58 publications

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“…Simple crossover [14] has been used and the best results have been obtained using a mutation probability of 10%. Fig.…”

Section: Resultsmentioning

confidence: 99%

“…Duplicates are not allowed in the population, so they are avoided in the initial population and also during the insertion in the evolving population along the execution of the algorithm. A simple crossover [14] is used. Selection is done by tournament selection, according to the selection probability of the individuals.…”

Section: A a Steady-state Genetic Algorithmmentioning

confidence: 99%

“…With elitism, the best individual of each generation is conserved and inserted directly in the next generation. A BLX-α with α = 0.4 [14] crossover is chosen. Selection is done by tournament according to the selection probability of the individuals.…”

Section: B a Generational Genetic Algorithmmentioning

confidence: 99%

See 2 more Smart Citations

Grid-connected PV-systems Joule design using Evolutionary Strategies

Gómez-Lorente¹,

Hernández²,

Dols³

et al. 2024

RE&PQJ

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Abstract. This paper shows how to compute the optimal distribution of the main elements of a photovoltaic (PV) plant with trackers connected to the network, to reduce some of the losses that occur in these facilities. In particular, the objective function of the proposed optimization process is the minimization of Joule losses that occur in electric conductors. To solve this problem, genetic algorithms (GAs), implemented in Matlab, have been used. Specifically the steady-state GA and the generational GA, which from random initial solutions, possibly far from optimal, will evolve into solutions that are increasingly closer to the desired optimum. Results have been very satisfactory, since all proposed examples have reduced by more than 50% electrical losses originated by not optimally designing the PV plant.

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“…Simple crossover [14] has been used and the best results have been obtained using a mutation probability of 10%. Fig.…”

Section: Resultsmentioning

confidence: 99%

Section: A a Steady-state Genetic Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Grid-connected PV-systems Joule design using Evolutionary Strategies

Gómez-Lorente¹,

Hernández²,

Dols³

et al. 2024

RE&PQJ

View full text Add to dashboard Cite

show abstract

“…• The crossover operator considered is the Parent Centric BLX (PCBLX) operator 30 , which is based on the BLX-α.…”

Section: Chc Algorithm: the Genetic Algorithm For Tuningmentioning

confidence: 99%

Genetic lateral tuning for subgroup discovery with fuzzy rules using the algorithm NMEEF-SD

Carmona¹,

González²,

Gacto³

et al. 2012

IJCIS

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The main objective of subgroup discovery is to discover interesting and interpretable patterns with respect to a specific property. The use of evolutionary fuzzy systems provides good algorithms to approach this problem. In this sense, NMEEF-SD algorithm -one of the most representative evolutionary fuzzy systems for subgroup discovery-obtains precise and interpretable subgroups. However in the majority of the evolutionary fuzzy systems, the membership functions of the linguistic labels are usually fixed to static values and the partitions are not adapted to the context of each variable. In this paper, a post-processing tuning step to improve the results of the subgroup discovery algorithm NMEEF-SD is proposed, allowing the partitions to be adapted to the context the variables. The application of this tuning step is a novelty in subgroup discovery and consist of a genetic algorithm which allows the lateral displacement of the membership functions of a label considering a unique parameter, using the 2-tuples linguistic representation. The results obtained using different data sets of the KEEL repository show the improvement in the performance of the NMEEF-SD algorithm with lateral displacement. The study is supported by statistical tests to improve the analysis performed.

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“…A quantum rotation based adaptive and parameter tuning free crossover operator has been designed by hybridizing quantum entanglement with modified BLX-α crossover operator [18]. The second qubits' amplitude is used for determining the angle of rotation for evolving the first qubit.…”

Section: Adaptive Quantum Inspired Crossover Operatormentioning

confidence: 99%

Analysis of Photoluminescence measurement data from interdiffused Quantum Wells by Real coded Quantum inspired Evolutionary Algorithm

Mani¹,

Patvardhan²

2011

Proceedings of 13th International Workshop on Advanced Computing and Analysis Techniques in Physics Research — PoS(ACAT2010)

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Abstract:Reliable analysis of any experimental data is always difficult due to the presence of noise and other types of errors. This paper analyzes data obtained from photoluminescence measurement, after annealing of interdiffused Quantum Well hetrostructures, using an Adaptive Real coded Quantum inspired Evolutionary Algorithm (ARQiEA). The proposed algorithm directly computes interdiffusion parameters without using Arrhenius plot. Further, the results obtained have been compared with those reported with Genetic Algorithm and Least Square Analysis methods. The ARQiEA is suitable for parameter estimation problems as it is free from parameter fine-tuning and provides result with least overall error.

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A taxonomy for the crossover operator for real-coded genetic algorithms: An experimental study

Cited by 339 publications

References 58 publications

Grid-connected PV-systems Joule design using Evolutionary Strategies

Grid-connected PV-systems Joule design using Evolutionary Strategies

Genetic lateral tuning for subgroup discovery with fuzzy rules using the algorithm NMEEF-SD

Analysis of Photoluminescence measurement data from interdiffused Quantum Wells by Real coded Quantum inspired Evolutionary Algorithm

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