Introduction to Crossover Strategy

What is/are Crossover Strategy?

Crossover Strategy - In aDE, a novel mutation and crossover strategy along with the slightly changed selection scheme are introduced, to avoid premature convergence. ^[1] This study aims to answer whether an optimized moving average crossover strategy based on daily data outperforms a buy and hold strategy. ^[2] Therefore, in this work, a generalized oppositional MFO with crossover strategy, named GCMFO, is presented to overcome the mentioned defects. ^[3] Based on the proposed local-global influence indicator, we propose a constrained evolutionary framework by designing several novel strategies such as mutation strategy, crossover strategy and repairment strategy to promote the evolution of population. ^[4] The performance of a differential evolution algorithm depends highly on its mutation and crossover strategy and associated control parameters. ^[5] The crossover strategy enabled by ligand exchange in an organometallic intermediate overcomes this reactivity issue, expanding the scope of carboboration to unactivated secondary alkyl halides, and opens new avenues to access stereodefined tetrasubstituted vinylboronates. ^[6] The crossover strategy enabled by ligand exchange in an organometallic intermediate overcomes this reactivity issue, thus expanding the scope of carboboration to unactivated secondary alkyl halides and opening new avenues to access of stereodefined tetrasubstituted vinylboronates. ^[7] In contrast, this paper proposes a unified GA approach that enables the application of an identical crossover strategy in both the categorical and sequential parts. ^[8] A forward matching strategy is proposed to find the matched important filters and a crossover strategy is proposed to utilize the important elements of the important filters for updating unimportant filters. ^[9] Location problem is a kind of resource allocation problem, so, on the basis of giving full play to the advantages of genetic algorithm in solving “allocation problem”, the improved genetic algorithm is proposed to solve the location problem through a series of improvement strategies such as the subsection coding strategy based on classification rules of transportation nodes, population initialization based on Halton sequence, multi-point sequential crossover strategy, and the mutation strategy based on gene-sequence. ^[10] In this work, we show how path-relinking can be applied in the space of the random-keys and successfully explored as a progressive crossover strategy in biased random-key genetic algorithms. ^[11] For the improvement of the cuckoo algorithm, the adaptive variable step-size factor is designed to realize the adaptive adjustment of flight step-size, and a mutation and crossover strategy is introduced to enhance the population diversity and improve the global optimization ability. ^[12] The IDP-GA could consider various objectives with normalized weighted sum method and could reduce the number of function calls with novel crossover strategy and vector-based pattern search method. ^[13] The CHC algorithm based on multi-point crossover strategy is used to further improve the accuracy of instance selection. ^[14] , the worst harmony learning from the best harmony and the random selected other harmony learning from the best harmony random crossover strategy. ^[15] The nonlinear convergence factor is designed to correct the scaling factor of FPA, the Tent chaotic map effectively maintains the diversity of the population in the late iteration, and a greedy crossover strategy is designed to assist the remaining individual search with better individuals. ^[16] Improvements that consist of the modification of historical population updating mechanism, the hybrid of mutation and crossover strategy of difference evaluation algorithm, a greedy local search algorithm and the re-initialization operator are also made to enhance the exploitation and exploration ability of IBSA. ^[17] The design of such trials is typically rooted in a simple crossover strategy with multiple intervention response evaluation periods. ^[18] For the second problem, a new crossover strategy as well as a hybridization of BSO with graph theory elements are proposed. ^[19] Parallelism among subpopulations and the proposed adaptive mutation and rotation crossover strategy can increase the speed of evolution without losing population diversity. ^[20] The proposed algorithm considers the balance between the exploitation and exploration processes by introducing (i) a single-based solution representation, (ii) a switchable mutation scheme, (iii) a vector-based estimation of the mutation factor, and (iv) an optional crossover strategy. ^[21] To study the optimization problem of wireless sensor network (WSN) based on differential evolution, the single objective differential evolution algorithm is applied and combined with the advantages and disadvantages crossover strategy. ^[22] In MOPNAR-II, a gene-oriented crossover strategy is utilized to perform extended crossover operation between two intervals, so as to improve the ability of exploitation, while the mutation of rules and the limitation of boundary are also improved. ^[23] While, Crossover strategy is applied in learner phase of TLBO algorithm to find good learner. ^[24]

crossover strategy enabled

The crossover strategy enabled by ligand exchange in an organometallic intermediate overcomes this reactivity issue, expanding the scope of carboboration to unactivated secondary alkyl halides, and opens new avenues to access stereodefined tetrasubstituted vinylboronates. ^[1] The crossover strategy enabled by ligand exchange in an organometallic intermediate overcomes this reactivity issue, thus expanding the scope of carboboration to unactivated secondary alkyl halides and opening new avenues to access of stereodefined tetrasubstituted vinylboronates. ^[2]