## What is/are Crossover Operators?

Crossover Operators - 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.^{[1]}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.

^{[2]}Results demonstrate significant improvements when using Partial-ACO as a mutation operator with a range of crossover operators.

^{[3]}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.

^{[4]}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.

^{[5]}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.

^{[6]}Three crossover operators for the PLA design optimization were proposed recently.

^{[7]}Ternary quantum logic based selection and crossover operators are introduced in this paper.

^{[8]}We study several mutation and crossover operators and evaluate them on real-world data of Berlin, Germany.

^{[9]}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.

^{[10]}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.

^{[11]}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.

^{[12]}Based on our test results, the 2-opt and Partially Matched Crossover operators are more efficient than the Order Crossover and Cycle Crossovers.

^{[13]}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.

^{[14]}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.

^{[15]}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.

^{[16]}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.

^{[17]}The GA technique combines Darwin’s principle of survival of fittest and a structured information exchange using randomized crossover operators to evolve an optimum design for the cementitious sandwich panel.

^{[18]}The experimental results with eighteen well-known models depict that our proposed EPX operator performs better than the other competitive crossover operators.

^{[19]}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.

^{[20]}Among various crossover operators, the simulated binary crossover (SBX) is the most widely used in evolutionary multi-objective optimization.

^{[21]}Besides, the mutation and crossover operators are utilized to achieve the discrete particle update process, thereby better solving the discrete TDXSMT problem.

^{[22]}We develop a fuzzy mathematical model (FMM) and a Genetic Algorithm (GA) with two crossover operators.

^{[23]}However, many crossover operators are problem-dependent and have different search abilities.

^{[24]}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.

^{[25]}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).

^{[26]}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.

^{[27]}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.

^{[28]}We design specific mutation and crossover operators for the evolution of DenseNet population.

^{[29]}However, a GA requires specialist crossover operators for permutation problems to avoid repetition.

^{[30]}In the matheuristic, we propose two crossover operators which exploit the structure of the problem.

^{[31]}Next to the definition and the formulation of the E-MDVRPTW-NL, this paper presents the evolutionary method for solving this problem using the Genetic Algorithm (GA), where a novel two-layer genotype with multiple crossover operators is considered.

^{[32]}These MOEAs use traditional crossover operators to create new candidate solutions through genetic recombination.

^{[33]}, mutation and crossover operators) used.

^{[34]}Single Point Crossover (SPC) and Two Point Crossover (TPC) are classic crossover operators that are easy to apply to ordinal representation coding rather than path representation coding.

^{[35]}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).

^{[36]}A key feature of GAs is crossover operators, which allow individuals in a population to communicate and exchange information with each other.

^{[37]}The efficiency of the newly proposed improved crossover strategies, combining multiple crossover operators with the progressive crossover strategy by path-relinking, is illustrated by applications on two problems: the single-round divisible load scheduling problem and the multi-round divisible load scheduling problem.

^{[38]}The proposed method improves the performance of DE by automatically selecting trial vector generation strategies (both mutation and crossover operators) and dynamically generating the associated control parameter values.

^{[39]}We design three novel crossover operators that consider problem-specific knowledge.

^{[40]}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.

^{[41]}Three new operators comprising a two-point swap, random insert, and half points crossover operators were introduced to discretized the algorithms.

^{[42]}The mutation and crossover operators abstracted from differential evolution with greedy strategy can better balance diversification and intensification during the optimization process.

^{[43]}To improve search efficiency, locally enhanced orthogonal crossover operators were introduced into the search process.

^{[44]}This paper will help the people to acquire the knowledge about various strategies of selecting parents and description about standard crossover operators.

^{[45]}A discrete differential evolution algorithm with new mutation and crossover operators is proposed to find near-optimal solutions of this problem.

^{[46]}Partition crossover operators return the best of 2k reachable offspring, where k is the number of recombining components.

^{[47]}The computational results as well as the comparison with available well-developed crossover operators are also presented.

^{[48]}The approach employs different mutation and crossover operators, which are chosen at random at each iteration.

^{[49]}We proposed a multi-objective genetic algorithm with new crossover operators to solve the problem.

^{[50]}

## Two Crossover Operators

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]}

## Novel Crossover Operators

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 design three novel crossover operators that consider problem-specific knowledge.

^{[2]}In this paper, we propose an EC-based algorithm with novel crossover operators to effectively address the above challenges.

^{[3]}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.

^{[4]}

## New Crossover Operators

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).^{[1]}We proposed a multi-objective genetic algorithm with new crossover operators to solve the problem.

^{[2]}Two new crossover operators, i.

^{[3]}

## Traditional Crossover Operators

These MOEAs use traditional crossover operators to create new candidate solutions through genetic recombination.^{[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]}This study is based on numerical experiments of the proposed with other traditional crossover operators for eighteen benchmarks TSPLIB instances.

^{[3]}

## Different Crossover Operators

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]}

## Partition Crossover Operators

Partition crossover operators return the best of 2k reachable offspring, where k is the number of recombining components.^{[1]}Partition crossover operators use information about the interaction between decision variables to recombine solutions.

^{[2]}

## Classic Crossover Operators

Single Point Crossover (SPC) and Two Point Crossover (TPC) are classic crossover operators that are easy to apply to ordinal representation coding rather than path representation coding.^{[1]}Moreover, on average, the proposed algorithm shows comparative performance in comparison with GA with classic crossover operators.

^{[2]}

## Competitive Crossover Operators

The experimental results with eighteen well-known models depict that our proposed EPX operator performs better than the other competitive crossover operators.^{[1]}The BBX operator is rapid and efficient which performs better than several classic and competitive crossover operators.

^{[2]}

## Point Crossover Operators

Three new operators comprising a two-point swap, random insert, and half points crossover operators were introduced to discretized the algorithms.^{[1]}Uniform and one-point crossover operators as well as two mutation operators conduct the search in the employed genetic algorithm.

^{[2]}

## Multiple Crossover Operators

Next to the definition and the formulation of the E-MDVRPTW-NL, this paper presents the evolutionary method for solving this problem using the Genetic Algorithm (GA), where a novel two-layer genotype with multiple crossover operators is considered.^{[1]}The efficiency of the newly proposed improved crossover strategies, combining multiple crossover operators with the progressive crossover strategy by path-relinking, is illustrated by applications on two problems: the single-round divisible load scheduling problem and the multi-round divisible load scheduling problem.

^{[2]}