## What is/are Crossover Operator?

Crossover Operator - The algorithm includes three main components: (i) a greedy randomized heuristic for population initialization; (ii) a dedicated local search procedure for local optima exploration; (iii) a backbone-based crossover operator for solution recombination.^{[1]}A crossover operator and a local search operator are designed to improve the exploration and exploitation of the algorithm, respectively.

^{[2]}In addition, a map-based crossover operator and three different mutation operators are proposed for the LR, and then a hybrid approach is implemented by utilizing those operators together.

^{[3]}The proposed variant termed an internal adaption-based environment is considered in the existing mutation and crossover operators to provide more diversity for selecting the effective mutant solutions.

^{[4]}Moreover, it finds the neighbor solutions based on the greedy repair and improvement mechanism and uses both mutation and crossover operators to explore and exploit the solution space.

^{[5]}Results demonstrate significant improvements when using Partial-ACO as a mutation operator with a range of crossover operators.

^{[6]}The design of our replication experiments employs three perspectives: duplicate the exact conditions using various features models, study the interaction of two parameters of the genetic programming approach, and optimize the values for the population and generation parameters and for the mutation and crossover operators.

^{[7]}Drawing lessons from the idea of greed, the superiority of the initial population is improved; the entry matrix and the exit matrix of the iterative population are constructed, and the crossover operator is improved based on this, and the forward insertion method is introduced to design the hybrid crossover operation to speed up the population.

^{[8]}We show that the VPMS algorithm integrating a variable population, an effective local optimization procedure, and a backbone-based crossover operator performs very well compared to state-of-the-art CNP algorithms.

^{[9]}Given the above, the crossover operator is integrated with the landmark component of the BPIO to improve the diversity of the solution space.

^{[10]}In the proposed EXQPSO algorithm, the crossover operator is performed on the elitist individuals to improve the qualities of the search individuals.

^{[11]}For this purpose, a hybrid algorithm is developed by imitating the algorithmic structure of FPA while the mutation and crossover operators of differential evolution (DE) with elitism strategy are replaced with Levy flights of FPA to explore search space more efficiently and exploitation ability of FPA is improved with the addition of mutation factor and crossover operator of DE working in the local search formula of FPA.

^{[12]}In this paper, an Improved Flower Pollination Algorithm with dynamic switch probability and crossover operator is proposed to solve these complex optimization problems.

^{[13]}We design a timely-vertices based crossover operator and mutation operator to give birth to the offspring with high quality and good structure built in the search process.

^{[14]}Secondly, the crossover operator of BSA with the adaptive component of mixrate is incorporated to leverage the entire active search regions visited previously.

^{[15]}Ternary quantum logic based selection and crossover operators are introduced in this paper.

^{[16]}For this NP-hard problem, we present a highly effective hybrid evolutionary algorithm (HEA) that integrates a crossover operator based on solution reconstruction, a destroy-and-repair mutation operator to generate multiple distinct offspring solutions, and a two-phase tabu search procedure to seek for high-quality local optima.

^{[17]}The double-Pareto probability density based crossover operator and a multiple standard deviation based Gaussian mutation scheme outperform their counterparts.

^{[18]}Detailed investigations indicate that both proposed schemes work very well to make ERA evolves in an exploitative manner, which is created by a high portion of high-quality individuals and the crossover operator, and avoids being trapped on the local optimum solutions in an explorative manner, which is generated by a high portion of low-quality individuals and the mutation operator.

^{[19]}The genetic section consists of a population of chromosomes, mutation operator, crossover operator, and a chromosome’s fitness function.

^{[20]}We study several mutation and crossover operators and evaluate them on real-world data of Berlin, Germany.

^{[21]}The optimal size of the system components is found using an improved crow search algorithm based on mutation and crossover operators of the genetic algorithm to prevent premature convergence.

^{[22]}In this study, two new multi-criteria and combinatorial ABC algorithms using mutation and crossover operators are proposed to generate a test suite that maximizes a fitness function combining various goals for object-oriented software.

^{[23]}In this study, an efficient hybrid evolutionary search algorithm (HESA) is proposed to tackle GQMKP, which relies on a knapsack-based crossover operator to generate new offspring solutions, and an adaptive feasible and infeasible tabu search to improve new offspring solutions.

^{[24]}The traditional genetic algorithm is improved in the initial population strategy and crossover operator.

^{[25]}Moreover, based on this multi-objective squirrel search algorithm, this paper then designs an encoding method to initialize solutions, applies a crossover operator to the squirrel migration process, and utilizes a mutation operator to the squirrel mutation stage.

^{[26]}This paper proposed an improved PSO with BSA called PSOBSA to resolve the original PSO algorithm’s problems that BSA’s mutation and crossover operators were modified through the neighborhood to increase the convergence rate.

^{[27]}We target quantifying such impacts through systematic benchmarking by investigating 28 major variants of Differential Evolution (DE) taken from the modular DE framework (by combining different mutation and crossover operators) and 13 commonly applied BCHMs, resulting in 28 X 13 = 364 algorithm instances after pairing DE variants with BCHMs.

^{[28]}The crossover operator is introduced to ensure the feasibility of the new generation of harmony, and the local search method is used to change the machine and tool resources available for the operation to avoid falling into the local optimum.

^{[29]}This work proposed a novel image encryption algorithm based on a logisticsine map and crossover operator of a genetic algorithm.

^{[30]}In each new chain, after the initial one, the crossover operator generates the initial solution.

^{[31]}In particular, the genetic algorithm is combined with the so-called hierarchical (self-similar) iterated tabu search algorithm, which serves as a powerful local optimizer (local improvement algorithm) of the offspring solutions produced by the crossover operator of the genetic algorithm.

^{[32]}(2) A new social learning approach combined with crossover operator is designed to ensure the diverse evolution of the swarm while maintaining the exploitation capability.

^{[33]}Estimation of Distribution Algorithms (EDAs) are interesting evolutionary methods; they have the characteristic of using an explicit distribution model, instead of mutation and crossover operators.

^{[34]}In this research, we propose a new real-coded based crossover operator by using the Exponentiated Pareto distribution (EPX), which aims to preserve the two extremes.

^{[35]}In order to improve the local search ability and computational speed of the algorithm, an adaptive genetic operator is used to dynamically adjust the crossover operator in the evolution process.

^{[36]}Finally, the crossover operator of the genetic algorithm is used to continuously synthesize new data samples to balance the data set.

^{[37]}By using TPG graph to represent the priority between tasks, the crossover operator and mutation operator are used to avoid falling into the local optimal solution, and the standardized method is used to prevent the employees from working overtime.

^{[38]}Besides, the mutation and crossover operators are utilized to achieve the discrete particle update process, thereby better solving the discrete TDXSMT problem.

^{[39]}The proposed algorithm combines a backbone-based crossover operator for generating offspring solutions and a responsive threshold search that alternates between a threshold-based exploration procedure and a descent-based improvement procedure for improving new offspring solutions.

^{[40]}The crossover operator, as an important technique to search for new solutions in GAs, has a strong impact on the final optimization results.

^{[41]}The evolutionary search applied to select IDS algorithm features can be developed by modifying and enhancing mutation and crossover operators and applying new enhanced techniques in the selection process, which can give better results and enhance the performance of intrusion detection for rare and complicated attacks.

^{[42]}In this paper, in order to improve both the exploitation and exploration abilities of the firefly algorithm (FA), a new modification approach based on the mutation and crossover operators as well as an adaptive formulation is applied as an adaptive modified firefly algorithm (AMFA).

^{[43]}This GAs consider an effective mixed continuous discrete coding method with a four point crossover operator, which take into account, the uncertainty on the demand using Gaussian process modeling.

^{[44]}The hyper-heuristics are employed to choose the crossover operator selected from a pool of operators, according to a probability that reflects the operator’s previous performance during the evolutionary process.

^{[45]}Unlike population-based meta-heuristics with employing mutation and crossover operators to generate trial populations, the proposed algorithm develops a reproduction operator by using the even difference grey model.

^{[46]}We design specific mutation and crossover operators for the evolution of DenseNet population.

^{[47]}Then, the crossover operator and leaping operator are applied in ABC, and the improved binary ABC is used for secondary pruning, and the decision trees with better performance are selected for voting.

^{[48]}In a used genetic algorithm with a global trend, the crossover operator is performed to explore search space.

^{[49]}The crossover operator of genetic algorithm is used to keep the directivity of particles, and the acceptance criterion of simulated annealing is used to enhance the ability of the algorithm to jump out of local optimum.

^{[50]}

## particle swarm optimization

This paper presents a new Hybrid Particle Swarm Optimization and Evolutionary algorithm (HPSO-E) to solve this problem by introducing a new population of children particles obtained by applying a mutation and crossover operators taken from the evolutionary algorithm.^{[1]}This paper proposes a particle swarm optimization algorithm with crossover operator (CPSO) to maximize the use of resources and reduce energy consumption.

^{[2]}Considering that the particle swarm optimization algorithm is easy to fall into the local optimum and the existing problems of using the cross and cross algorithm to improve the particle swarm optimization algorithm, the vertical crossover operator based on the CSO re-improves the particle swarm optimization algorithm.

^{[3]}Genetic algorithm–related operators including a selection operator with time-varying selection probability, crossover operator, and n-point random mutation operator are incorporated in the particle swarm optimization algorithm to further exploit optimal solutions generated by the particle swarm optimization algorithm.

^{[4]}As the common proportional integral derivative (PID) controller has the disadvantages of overshoot and excessive adjusting time, an integral separation PID (IPID) control algorithm optimized by modified particle swarm optimization (PSO) based on dynamic weight and crossover operator (MPSO-IPID) is proposed in this paper.

^{[5]}In this paper, a novel hybrid partitional clustering algorithm is proposed, named IDKPSOC-k-means, based on an improved self-adaptive Particle Swarm Optimization (PSO) and K-Means, which uses a crossover operator to improve PSO capability to escape from local minima points from the problem space.

^{[6]}

## differential evolution algorithm

Then, in order to solve this multiobjective game, an adaptive differential evolution algorithm based on simulated annealing (ADESA) is proposed to solve this game, which is to improve the mutation factor and crossover operator of the differential evolution (DE) algorithm adaptively, and the Metropolis rule with probability mutation ability of the simulated annealing (SA) algorithm is used.^{[1]}A discrete differential evolution algorithm with new mutation and crossover operators is proposed to find near-optimal solutions of this problem.

^{[2]}Crossover operator is the main part of the genetic algorithm while a mutation operator is the main part of the differential evolution algorithm.

^{[3]}An improved differential evolution algorithm is then built which utilizes the standard DE algorithm from the F value of the mutation operator and the crossover operator CR value to enhance the mutation strategy and ultimately obtain a near-optimized solution to the problem.

^{[4]}

## real coded genetic

Considering the slowness of convergence rate when the road network is large, a modified matrix real-coded genetic algorithm is designed with the crossover operator based on a greedy algorithm.^{[1]}In order to solve the large-scale, strong coupling, and nonlinear optimization problems in many Internet-of-Things (IoT) applications, such as intelligent infrastructure and smart city, this article proposes a real-coded genetic algorithm based on an auxiliary-individual-directed crossover operator (AIDX).

^{[2]}To feasibly and reliably obtain geotechnical parameters for the surrounding rock (which vary in different places), a real-coded genetic algorithm is used in setting the initial parameters of the neural network to improve the prediction accuracy of the parameters via back analysis by reasonably selecting the selection operator, crossover operator, and mutation operator.

^{[3]}

## improved genetic algorithm

Compared with the design scheme in the iterative process of traditional optimization design, the improved genetic algorithm introduces the multi parent crossover operator, randomly selects N individuals, forms the subspace, and hybridizes N individuals in the subspace to form a new individual.^{[1]}In order to accurately estimate international financial risks, improve the risk management performance of financial institutions, and ensure the sustainable development of the international financial market, an international financial risk estimation model based on improved genetic algorithms was designed, the value-at-risk model VAR model was selected to estimate the international financial risk by measuring the degree of economic loss, and the improved genetic algorithm was adopted to the seven parts of immature convergence to quickly obtain the VAR value of international financial risks, including initialize the population, real number coding, determine fitness function, selection operator, crossover operator, mutation operator and predict and process.

^{[2]}The basic idea of the improved genetic algorithm is to change the coordination mode of the crossover operator and the mutation operator according to the size of the initial population fitness.

^{[3]}

## 2 opt method

This research will present an enhancement for genetic algorithm by using a 2-opt method to solve TSP, such that we will use a 2-opt method to generate the initial population for genetic algorithm, and the roulette wheel choice strategy, the order crossover operator and 2-opt method for mutation operator will be highlighted.^{[1]}

## New Crossover Operator

In this paper a new crossover operator closest-node pairing crossover (CNPC) is recommended which is explicitly designed to improve the performance of the genetic algorithm compared to other well-known crossover operators such as point based crossover and order crossover.^{[1]}Therefore, in this paper we develop a multiobjective evolutionary algorithm (MOEA) that integrates an improved construction algorithm and a new crossover operator for efficient distribution services.

^{[2]}It combines the diversification phase of generating new local optima found after solutions modified by a new crossover operator that is biased towards one of the parents, with the intensification phase of an effective tabu search which uses a simplified tabu list structure to reduce the number of parameters and a new long-term memory that saves solutions previously visited to speed up the search.

^{[3]}This paper proposes new crossover operators for AsFault that can better preserve the coupling between genotype (representations of road segments) and phenotype (occurrences of interesting self-driving behaviour).

^{[4]}Therefore, the research presents a new crossover operator for TSP, allowing the further minimization of the total distance.

^{[5]}We proposed a multi-objective genetic algorithm with new crossover operators to solve the problem.

^{[6]}Two new crossover operators, i.

^{[7]}At the same time, the new crossover operator and individual update strategy are used to further improve the convergence ability of the algorithm while maintaining a strong diversity of the population.

^{[8]}At the same time, the new crossover operator and individual update strategy are used to further improve the convergence ability of the algorithm while maintaining a strong diversity of the population.

^{[9]}In genetic algorithm, by using two instances which contained heterogeneous fleet and applying a new crossover operator, the number of used buses and the global cost decreased consequently.

^{[10]}This paper presents a modified genetic algorithm (GA) using a new crossover operator (ADX) and a novel statistic correlation mutation algorithm (CAM).

^{[11]}In addition, a new crossover operator is introduced to improve the evolving process of an evolutionary algorithm and hence its performance.

^{[12]}In DC-MA, a new crossover operator and a mutation operator are designed to optimize the target, control the density and ensure the stability; the local search is used to improve the accuracy and a special self-learning operator is proposed to adjust density.

^{[13]}In this article, the new crossover operator BBX for Evolutionary Algorithms (EAs) for traveling salesman problems (TSPs) is introduced.

^{[14]}Two new crossover operator and the improved mutation operator is used to achieve the evolution of the population.

^{[15]}Secondly, besides using traditional mutation operator, we propose a new crossover operator (called two-way random crossover operator in this paper) and a new mutation operator (called mutation operator 2 in this paper), which are helpful to improve the accuracy of the solution and accelerate the convergence speed of the proposed algorithm.

^{[16]}

## Novel Crossover Operator

Hence, in this paper, MLCNN architectures are optimized by implementing a novel approach Modified Genetic Algorithm (MGA) with the help of introducing four novel crossover operators to strengthen CNN performance for users interest classification.^{[1]}We propose MFRSEA , a multifactorial evolutionary algorithm utilizing a network random key representation, a constraint-aware fitness function, and a novel crossover operator in order to optimize for both network types simultaneously.

^{[2]}We design three novel crossover operators that consider problem-specific knowledge.

^{[3]}This paper proposes a novel crossover operator and mutation operator.

^{[4]}An MA that incorporates the use of iterated local search and a novel crossover operator is designed.

^{[5]}This study introduced the Inversed Bi-segmented Average Crossover (IBAX), a novel crossover operator that enhanced the offspring generation of the genetic algorithm (GA) for variable minimization and numerical optimization problems.

^{[6]}Then, we apply our ROCKS with novel crossover operator and mutation operator to optimize the robustness of the scale-free topologies constructed for WSNs.

^{[7]}In this paper, we propose an EC-based algorithm with novel crossover operators to effectively address the above challenges.

^{[8]}Based on this model, an evolutionary multiobjective robust scheduling algorithm is suggested, in which solutions obtained by a variant of single-objective heuristic are incorporated into population initialization and two novel crossover operators are proposed to take advantage of nondominated solutions.

^{[9]}In this paper, we will propose a Memetic Algorithm (MA) with a novel crossover operator to solve the distributed DWSC problem.

^{[10]}

## Adaptive Crossover Operator

Furthermore, a new adaptive crossover operator, which is based on the k-means algorithm, is proposed.^{[1]}The IIGA presents a new way of population initialization, a regulatory mechanism of antibody concentration, and a design method of adaptive crossover operator and mutation operator, which effectively improved the convergence ability and optimization anility of IIGA.

^{[2]}An adaptive crossover operator and mutation operator were constructed for a rapid convergence speed.

^{[3]}Firstly, an improved GA (IGA) is presented by integrating an adaptive crossover operator and three kinds of manual intervention measures into the standard GA to further improve the global searching ability.

^{[4]}In this paper, an improved two population genetic algorithm is proposed, in which a adaptive crossover operator is set in one population and a big mutation operator is set in another population to improve the existing genetic algorithm, so that the algorithm can balance the local search and global search ability.

^{[5]}An adaptive crossover operator and a novel topological replace operator invoked in this algorithm are described.

^{[6]}A Quantum Rotation inspired Adaptive Crossover operator is used as a variation operator, which is parameter free.

^{[7]}The PAGA-BP model ameliorates selection sorting operator, adaptive crossover operator and u-adaptive mutation operator to optimize the initial weight of BP neural network.

^{[8]}The adaptive crossover operator and mutation operator are proposed for the dynamic searching area by adaptively relocating the target according to the current searching results.

^{[9]}In the improved batch learning procedure, improved immune operator, improved self-adaptive crossover operator and mutation operator are embedded to simplified hill climbing algorithm, so that obtain a best structure which has better performance in accuracy and efficiency, at the same time a function is constructed to monitor new data stream.

^{[10]}

## Improved Crossover Operator

Thirdly, the genetic algorithm based on improved crossover operators is used to determine the optimal operational path, which effectively improves the iterative efficiency of the classical genetic algorithm and avoids falling into local convergence.^{[1]}In this paper, a new method of fuzzy-genetic algorithm based on bilinear membership functions is proposed with an improved crossover operator and penalty function.

^{[2]}An improved crossover operator for the FSGA has been developed for obtaining higher accuracies compared with other existing crossover operators.

^{[3]}Moreover, an improved crossover operator based on the partial-mapped crossover (PMX) is designed for the distributed scheduling problem.

^{[4]}An improved crossover operator is proposed, which provides two options, one-point crossover or group-level crossover.

^{[5]}

## Point Crossover Operator

This algorithm makes comprehensive improvements to the PSO clustering algorithm by using the K-means clustering algorithm to generate initial clustering centers, adopting a negative exponential function model to update the weight of velocity when constructing the “position-velocity” model, and introducing the “search restriction” mechanism, and the “fly-back” mechanism and auxiliary search methods such as the single point crossover operator in the Artificial Bee Colony (ABC) algorithm.^{[1]}Three new operators comprising a two-point swap, random insert, and half points crossover operators were introduced to discretized the algorithms.

^{[2]}A multi-objective particle swarm optimization (MOPSO) is designed using a mutation function, followed by a nondominated sorting genetic algorithm (NSGA-II) with a one-point crossover operator and a heuristic mutation operator.

^{[3]}The new approach combines random swap and random insertion methods for neighborhood search, the two-point crossover operator, and the Pareto-based method.

^{[4]}Uniform and one-point crossover operators as well as two mutation operators conduct the search in the employed genetic algorithm.

^{[5]}

## Partition Crossover Operator

Additionally, the partition crossover operator exploits the structure of the residue interaction graph in order to efficiently mix solutions and find new unexplored basins.^{[1]}Additionally, the partition crossover operator exploits the structure of the residue interaction graph in order to efficiently mix solutions and find new unexplored basins.

^{[2]}Partition crossover operators return the best of 2k reachable offspring, where k is the number of recombining components.

^{[3]}Partition crossover operators use information about the interaction between decision variables to recombine solutions.

^{[4]}

## Two Crossover Operator

We develop a fuzzy mathematical model (FMM) and a Genetic Algorithm (GA) with two crossover operators.^{[1]}In the matheuristic, we propose two crossover operators which exploit the structure of the problem.

^{[2]}In this work, we present the first population-based hybrid evolutionary search algorithm for solving the problem that combines: (i) a randomized greedy construction method for initial solution generation, (ii) a dedicated variable neighborhood search for local optimization, (iii) two crossover operators for solution recombination with an adaptive rule for crossover selection.

^{[3]}Simultaneously, two crossover operators are designed to enhance the communication between the low-grade wolves.

^{[4]}

## Heuristic Crossover Operator

Indirect path coding of the Genetic algorithm is used to extend the search range of the state space, and a heuristic crossover operator is used to generate more effective diagnostic paths.^{[1]}Motivated by these considerations, an improved differential evolution is proposed, which is based on the oppositional solution, elite sharing schemes, the heuristic crossover operator and combined with a self-adaptive parameter setting strategy.

^{[2]}This paper proposes a Brain Storm optimization (BSO) algorithm based on prior knowledge and heuristic crossover operator (PKHDBSO) to solve TSP.

^{[3]}The heuristic crossover operator, which is based on the local superior genetic information of the parent to generate new individuals, was designed to improve the search efficiency of the algorithm.

^{[4]}

## Uniform Crossover Operator

Specifically, a probability-based uniform crossover operator called “PEPPX” is adopted and the updating rule for the parameter of choosing probability is well tuned to balance the exploration and exploitation in different stages of the iterations.^{[1]}Invoking the genetic uniform crossover operator in the standard crow search algorithm can increase the diversity of the search and help the algorithm to escape from trapping in local minima.

^{[2]}These networks are trained with ELM and an application specific GA evolves them into heterogeneous networks according to a fitness criterion utilizing the uniform crossover operator for the recombination process.

^{[3]}In the literature, there exist several alternatives corresponding to each aspect of configuring a population-based algorithm such as one-point/two-points/uniform crossover operators, tournament/ranking/stochastic uniform sampling selection methods, Gaussian/Levy/Cauchy mutation operators, clearing/crowding/sharing based niching algorithms, adaptive penalty/epsilon/superiority of feasible constraint handling approaches, associated parameter values and so on.

^{[4]}

## Use Crossover Operator

Most of EMO algorithms use crossover operator for generating new solutions.^{[1]}This study provided an important opportunity to advance the understanding of the best method to use crossover operators for this highly-constrained optimisation problem effectively.

^{[2]}However, a concern about the NSGA-III algorithm is that it uses crossover operator for real-value initialized population.

^{[3]}

## Arithmetic Crossover Operator

Second, an improved arithmetic crossover operator is proposed to improve the global searching performance.^{[1]}Meanwhile, the BBO/current-to-select/1, which is a rotationally invariant arithmetic crossover operator, is designed to alleviate the rotational variance.

^{[2]}An improved strength Pareto evolutionary algorithm 2, AHSPEA2, is developed to solve this proposed model, whose search ability and population diversity are enhanced greatly by two strategies, the arithmetic crossover operator and the improved hybrid self-adaptive mutation operators.

^{[3]}

## Specific Crossover Operator

While keeping in view of grouping aspects of the problem, each individual, in the proposed SSGGA, is encoded as a group of rainbow trees, and accordingly a problem-specific crossover operator is designed.^{[1]}The method is characterized by an improved initialization strategy and a problem-specific crossover operator.

^{[2]}The population-based memetic algorithm combines a problem-specific crossover operator to generate meaningful offspring solutions, an iterated tabu search procedure to improve the offspring solutions, and a distance-quality-based pool updating strategy to maintain a healthy diversity of the population.

^{[3]}

## Hybrid Crossover Operator

The proposed SI algorithm incorporates hybrid crossover operators implemented by sine, cosine, and tanh functions for multiple elite offspring signal generation, as well as geometric search coefficients extracted from a three-dimensional super-ellipse surface.^{[1]}Thus, an improved multi-objective genetic algorithm using infeasible solution guidance and hybrid crossover operator of cytoplasm and chromosome is proposed.

^{[2]}A new hybrid crossover operator is designed to enhance the search ability of the proposed EMA and avoid premature convergence.

^{[3]}

## Different Crossover Operator

Due to the NP-hardness of the studied problem, and encouraged by the successful adaptation of metaheuristics for green scheduling problems, three genetic algorithms (GAs) using three different crossover operators and a simulated annealing algorithm (SA) were developed for large-sized problems.^{[1]}As different crossover operators have a unique bias in generating offspring, the appropriate configuration of crossover for knowledge transfer in MFEA is necessary toward robust search performance, for solving different problems.

^{[2]}This research aims to develop genetic algorithms (GAs) that considers six different crossover operators separately in order to find optimal solutions, and then compare GAs using different crossover operators.

^{[3]}

## Effective Crossover Operator

Moreover, we design an effective crossover operator based on retaining the inter-links of adjacent nodes and a local search operator according to the degree of nodes.^{[1]}Our new algorithm, called MOBICK, uses an efficient solution encoding, an effective crossover operator, and a heuristic mutation strategy.

^{[2]}

## Genetic Crossover Operator

For the third issue, we introduce genetic crossover operator to improve diversity in a recent Newtonian law of gravity-based metaheuristic binary gravitational search algorithm (BGSA) in multi-objective optimization scenario; it is named as improved multi-objective BGSA for feature selection (IMBGSAFS).^{[1]}In this paper we propose using a genetic crossover operator for the task of detecting the optimal queue of a product parts.

^{[2]}

## Proposed Crossover Operator

We also show that the proposed crossover operator outperforms a few existing ones in both fitness values and search times.^{[1]}The time complexity for training the RBFN is O(N^2), while the time complexity for applying the proposed crossover operator is O(N), the same time complexity of traditional crossover operators.

^{[2]}

## Oriented Crossover Operator

Oriented genetic algorithm includes an oriented crossover operator which directs generation of offspring.^{[1]}Auxiliary individual oriented crossover operator adopts oriented crossover framework and assistant individual technique which is beneficial to multidimensional network problem.

^{[2]}

## Binary Crossover Operator

Moreover, mutant vectors employ the binary crossover operator to generate the trial vectors through a crossover control parameter pool in^{[1]}Firstly, a new binary mutation operator and a binary crossover operator are designed.

^{[2]}