Oriented Crossover in Genetic Algorithms: Comparison
Please note this is a comparison between Version 2 by Camila Xu and Version 1 by Zahir Hussain.

A genetic algorithm is a formula for resolving optimization issues that incorporate a constraint and natural selection similar to the biological process that

propels evolution.

• genetic algorithm
• uniform crossover
• network protocol optimization

## 1. Introduction

A genetic algorithm is a formula for resolving optimization issues that incorporate a constraint and natural selection similar to the biological process that propels evolution. The recent addition of the genetic algorithm (GA) to artificial intelligence was motivated by the biological behavior of chromosomes [1]. The Darwinian evaluation principle known as “survival of the fittest” is what the evolution algorithms do [1]. Therefore, the goal of employing GA is to produce the best offspring (solution), which increases the necessity of adopting it. The GA begins with two parents and mates them to generate new offspring; this mating is termed the “crossover.” then the old population is replaced with the new one by using the crossover and mutation operators. This process continues until the convergence condition is met [2].

### 1.1. GA Operands

There are three operands [3] in a typical GA as follows.
• Selection: This operand determines which chromosomes of the population are selected for reproduction. If a chromosome fits better, it is more likely to be selected for reproduction.
• Crossover: This operator exchanges the subsequences between two chromosomes before and after a locus that is randomly chosen to create two offspring [3].
• Mutation: This procedure involves flipping one or more randomly selected bits in the parent’s chromosomes to create an offspring from a single parent [4]. Any bit has a slight chance of mutating, like 0.001 [3]. The layout shown in Figure 1 represents the typical GA process.
Figure 1.
Typical genetic algorithm design.
This approach is based on the observation that specific chromosomally encoded traits are shared by individuals and can be passed to )inherited by( their offspring through crossover [5]. The genes of either one parent or both parents, with mutations, are shared by two offspring.

### 1.2. Single–Point Crossover

The best-known and frequently applied crossover model so far among researchers is that presented by Ref. [6]. A crossover site is randomly selected along the length of the matched strings, and bits that are immediately near the cross-sites are exchanged.
The beneficial traits of the parents may be combined to produce better offspring when the right site is chosen. If the right place is selected when good parents are mated, the offspring will be better; if not, the string quality will be severely hampered. If the head and tail of one chromosome contain acceptable genetic material, then no offspring will acquire the two beneficial traits straight after the single-point crossover.

### 1.3. N-Point Crossover

Ref. [7] was the first to use the n-point crossover. It was similar to the single-point crossover. In a two-point crossover, there are two relevant crossing sites. The performance of the genetic algorithms can be severely impacted by interruptions of building blocks caused by the continual addition of the crossover sites.

### 1.4. Uniform Crossover

Ref. [8] presented a uniform crossover, where the chromosomes are not broken up by uniform crossover for recombination. Each gene in a child’s offspring is made by copying it from a parent who has been selected based on the bit that corresponds to it in a binary crossover mask that has the same length as the parent chromosomes. The two parents are chosen for crossover through the uniform crossover; it produces two children with n genes uniformly chosen from both parents. A random real integer determines whether the first child chooses the ith gene from the first or second parent [9].

### 1.5. Numerical Chromosome Representation

The crossover mostly used binary encoded chromosomes, and for real-value encoding, the numerical crossover is utilized. Here, two parent chromosomes are combined linearly by the numerical crossover operator. Two chromosomes are randomly chosen for crossover, resulting in two children who are a linear blend of their parents. N-point is mainly used in the case of binary encoded chromosomes [1].

### 1.6. Operators Definition

Numerous operators represent the main parts of a genetic algorithm form. A gene is a string of bits or a real number within a specific length. A chromosome is a term used to describe a sequence of genes. An allele, which can be represented by a symbol or a bit, is the smallest chromosomal unit. While a phenotype offers an external description of the individual, a genotype is a piece of data contained in a chromosome [4,10][4][10]. The main operands of GA are depicted in Figure 2.
Figure 2.
The GA operators.

## 2. Genetic Algorithms for Computer Networks Optimization

### 2.1. Original Theories

Ref. [8] was first to present the uniform crossover for GA, even at one point, the second point was presented, but the uniform crossover showed it outperform in optimization, and till now, this crossover type is applicable in many different science fields. Ref. [7] presented an adaptive algorithm to decide when a particular crossover (one point, second point, or uniform) will be optimal for any problem. However, it still works with the standard crossover. Ref. [9] presented an excellent review showing more than thirty-five types of crossovers presented till 2015, and all the suggested research used in different optimization fields. Ref. [4] presented a review that shows the importance of GA in the optimization for machine learning and deep learning. A schema that included a two-point crossover was published in Ref. [2], where the proposed methods offer a contrastive convergence rate. When the balance between the traits of parents and offspring was a challenge in GA optimization, Ref. [5] presented balanced crossover operators that guarantee the offspring has the same balanced features as the parents. Ref. [4] presented a modified optimization method that depended on AI, and presented guidance for both beginner and experienced researchers designing evolutionary neural networks, assisting them in selecting appropriate genetic algorithm operator values for use in applications in a certain issue domain.

### 2.3. Optimization in Fields of Science

The field of cloud computing improvement and WSN is getting a lot of attention among other fields, where the energy consumption of nodes in the network should be restricted. Therefore, Ref. [26] used an optimization technique to reduce the utilization of the energy by the data center in a way to accomplish the best quality of service. Refs. [27,28,29][27][28][29] presented an energy-efficient deployment method for sensor nodes by clustering the nodes and applying an optimization technique for the optimal reduction of the nodes’ battery. The optimization for nodes led to the optimization of the routing protocol. Ref. [30] used a GA for mechanical engineering in multi-scale surface roughness, and presented three genetic algorithms to superimpose and merge the mathematical descriptions of chromosomes to determine the best roughness features.

References

1. Kora, ; Yadlapalli, P. Crossover operators in genetic algorithms: A review. Int. J. Computer Applications 2017, 162, pp.34-36.
2. Hussain, ; Muhammad, Y.S.; Sajid, M.N. An Efficient Genetic Algorithm for Numerical Function Optimization with Two New Crossover Operators. Int. J. Math. Sci. Comput. 2018, 4, 41–55.
3. Mitchell, An Introduction to Genetic Algorithms; MIT Press: Cambridge, MA, USA, 1998.
4. Chiroma, H.; Abdulkareem, S.; Abubakar, A.; Herawan, T. Neural networks optimization through genetic algorithm searches: A Appl. Math. Inf. Sci. 2017, 11, 1543–1564.
5. Manzoni, ; Mariot, L.; Tuba, E. Balanced crossover operators in genetic algorithms. Swarm Evol. Comput. 2020, 54, 100646.
6. Holland, H. Adaptation in Natural and Artificial Systems; University of Michigan Press: Ann Arbor, MI, USA, 1975.
7. Spears, Adapting Crossover in a Genetic Algorithm; Technical Report; Naval Research Laboratory: Washington, DC, USA, 1994.
8. Syswerda, Uniform Crossover in Genetic Algorithms. In Proceedings of the 3rd International Conference on Genetic Algorithms, Fairfax, VA, USA, 2–9 June 1989; Morgan Kaufmann Publishers Inc., 340 Pine Street: San Francisco, CA, USA, 1989.
9. Umbarkar, J.; Sheth, P.D. Crossover operators in genetic algorithms: A review. ICTACT J. Soft Comput. 2015, 6.
10. Sivanandam, ; Deepa, S. Genetic Algorithms. In Introduction to Genetic Algorithms; Springer: Berlin/Heidelberg, Germany, 2008; pp. 15–37.
11. Kirchner-Bossi, ; Porté-Agel, F. Realistic wind farm layout optimization through genetic algorithms using a Gaussian wake model. Energies 2018, 11, 3268.
12. Félix Patrón, S.; Botez, R.M. Flight trajectory optimization through genetic algorithms coupling vertical and lateral profiles. in ASME International Mechanical Engineering Congress and Exposition, Montreal, Quebec, Canada. Am. Soc. Mech. Eng. 2014, 46421.
13. Bani-Hani, D.; Khan, N.; Alsultan, F.; Karanjkar, S.; Nagarur, N. Classification of leucocytes using convolutional neural network optimized through genetic In Proceedings of the 7th Annual World Conference of the Society for Industrial and Systems Engineering, Binghamton, NY, USA, 11–12 October 2018.
14. Zeidabadi, A.; Dehghani, M. Poa: Puzzle optimization algorithm. Int. J. Intell. Eng. Syst. 2022, 15, 273–281.
15. Anju, ; Avanish, K. An advanced approach to the employee recruitment process through genetic algorithm. Int. J. Inf. Technol. 2021, 13, 313–319.
16. Begum, ; Padmannavar, S.S. Genetically Optimized Ensemble Classifiers for Multiclass Student Performance Prediction. Int. J. Intell. Eng. Syst. 2022, 15, 316–328.
17. Haldulakar, ; Agrawal, J. Optimization of association rule mining through genetic algorithm. Int. J. Comput. Sci. Eng. (IJCSE) 2011, 3, 1252–1259.
18. Muthana, A.; Ku-Mahamud, K.R. Comparison of Multi-objective Optimization Methods for Generator Maintenance Scheduling. Methods (Multi-Object. Metaheuristics) 2022, 14, 15.
19. Rabee, ; Jazaery, I.; Kumar, K. Quaternary-Child Crossover for Genetic Algorithm in Real-Time Scheduling Optimization. Int. J. Intell. Eng. Syst. 2023, 16, 100–114.
20. Abdulkadhim, F.G.; Yi, Z.; Onaizah, A.N.; Rabee, F.; Al-Muqarm AM, A. Optimizing the Roadside Unit Deployment Mechanism in VANET with Efficient Protocol to Prevent Data Wirel. Pers. Commun. 2022, 127, 815–843.
21. Lu, F.; Bi, H.; Huang, M.; Duan, S. Simulated Annealing Genetic Algorithm Based Schedule Risk Management of IT Outsourcing Math. Probl. Eng. 2017, 2017, 6916575.
22. Pandey, M.; Chaudhary, A.; Mehrotra, D. Grammar induction using bit masking oriented genetic algorithm and comparative analysis. Appl. Soft Comput. 2016, 38, 453–468.
23. Bi, H.; Lu, F.; Duan, S.; Huang, M.; Zhu, J.; Liu, M. Two-level principal–agent model for schedule risk control of IT outsourcing project based on genetic Eng. Appl. Artif. Intell. 2020, 91, 103584.
24. Yan, T.; Lu, F.; Wang, S.; Wang, L.; Bi, H. A hybrid metaheuristic algorithm for the multi-objective location-routing problem in the early post-disaster J. Ind. Manag. Optim. 2022, 19, 4663–4691.
25. Abdul-Sada, H.; Rabee, F. The Genetic Algorithm Implementation in Smart Contract for the Blockchain Technology. Al-Salam J. Eng. Technol. 2023, 2, 37–47.
26. Singh, An Optimal Resource Provisioning Scheme Using QoS in Cloud Computing Based Upon the Dynamic Clustering and Self-Adaptive Hybrid Optimization Algorithm. Int. J. Intell. Eng. Syst. 2022, 15.
27. Sanapala, K.; Duggirala, S.R. An Optimized Energy Efficient Routing for Wireless Sensor Network using Improved Spider Monkey Optimization Algorithm. Transportation 2022, 8, 9.
28. Jubair, A.M.; Hassan, R.; Aman, A.H.; Sallehudin, H.; Al-Mekhlafi, Z.G.; Mohammed, B.A.; Alsaffar, M.S. Optimization of Clustering in Wireless Sensor Networks: Techniques and Appl. Sci. 2021, 11, 11448.
29. Rangappa, H.; Dyamanna, G.C. Energy-Efficient Routing Protocol for Hybrid Wireless Sensor Networks Using Falcon Optimization Algorithm. Int. J. Intell. Eng. Syst. 2022, 15.
30. Cinat, ; Gnecco, G.; Paggi, M. Multi-scale surface roughness optimization through genetic algorithms. Front. Mech. Eng. 2020, 6, 29.
31. Forouzan, A. TCP/IP Protocol Suite, 4th ed.; McGraw-Hill Higher Education: New York, NY, USA, 2010.
32. Singh, ; Chaurasia, P. A Review on Genetic Algorithm Operations and Application in Telecommunication Routing. International Journal of Computer Sciences and Engineering. 2019, 7(7), 273-277.
33. Kwon, C. Julia Programming for Operations Research 2022, 2nd , University of South Florida, USA, 2022.
34. Schärfe, P.I.; Tremmel, R.; Schwab, M.; Kohlbacher, O.; Marks, D.S. Genetic variation in human drug-related genes. Genome Med. 2017, 9, 117.
35. King, A.; Davis, J.W.; Degner, J.F. Are drug targets with genetic support twice as likely to be approved? Revised estimates of the impact of genetic support for drug mechanisms on the probability of drug approval. PLoS Genet. 2019, 15, e1008489.
36. Ortiz-Boyer, ; Hervás-Martínez, C.; Garcia-Pedrajas, N. Cixl2: A crossover operator for evolutionary algorithms based on population features. J. Artif. Intell. Res. 2005, 24, 1–48.
37. Li, ; Engelbrecht, A.; Epitropakis, M.G. Benchmark Functions for CEC’2013 Special Session and Competition on Niching Methods for Multimodal Function Optimization; Evolutionary Computation and Machine Learning Group, Australia, Tech. Rep., RMIT University, Melbourne, Australia, 2013.
38. Khudhair, M.M.; Adil, A.R.; Rabee, F. An innovative fractal architecture model for implementing MapReduce in an open multiprocessing parallel Indones. J. Electr. Eng. Comput. Sci. 2023, 30, 1059–1067.‏
39. Khudhair, M.M.; Rabee, F.; AL-Rammahi, A. New efficient fractal models for MapReduce in OpenMP parallel Bull. Electr. Eng. Inform. 2023, 12, 2313–2327.‏

### References

1. Kora, P.; Yadlapalli, P. Crossover operators in genetic algorithms: A review. Int. J. Comput. Appl. 2017, 162, 34–36.
2. Hussain, A.; Muhammad, Y.S.; Sajid, M.N. An Efficient Genetic Algorithm for Numerical Function Optimization with Two New Crossover Operators. Int. J. Math. Sci. Comput. 2018, 4, 41–55.
3. Mitchell, M. An Introduction to Genetic Algorithms; MIT Press: Cambridge, MA, USA, 1998.
4. Chiroma, H.; Abdulkareem, S.; Abubakar, A.; Herawan, T. Neural networks optimization through genetic algorithm searches: A review. Appl. Math. Inf. Sci. 2017, 11, 1543–1564.
5. Manzoni, L.; Mariot, L.; Tuba, E. Balanced crossover operators in genetic algorithms. Swarm Evol. Comput. 2020, 54, 100646.
6. Holland, J.H. Adaptation in Natural and Artificial Systems; University of Michigan Press: Ann Arbor, MI, USA, 1975.
7. Spears, W. Adapting Crossover in a Genetic Algorithm; Technical Report; Naval Research Laboratory: Washington, DC, USA, 1994.
8. Syswerda, G. Uniform Crossover in Genetic Algorithms. In Proceedings of the 3rd International Conference on Genetic Algorithms, Fairfax, VA, USA, 2–9 June 1989; Morgan Kaufmann Publishers Inc., 340 Pine Street: San Francisco, CA, USA, 1989.
9. Umbarkar, A.J.; Sheth, P.D. Crossover operators in genetic algorithms: A review. ICTACT J. Soft Comput. 2015, 6, 1083–1092.
10. Sivanandam, S.; Deepa, S. Genetic Algorithms. In Introduction to Genetic Algorithms; Springer: Berlin/Heidelberg, Germany, 2008; pp. 15–37.
11. Kirchner-Bossi, N.; Porté-Agel, F. Realistic wind farm layout optimization through genetic algorithms using a Gaussian wake model. Energies 2018, 11, 3268.
12. Félix Patrón, R.S.; Botez, R.M. Flight trajectory optimization through genetic algorithms coupling vertical and lateral profiles. In Proceedings of the ASME International Mechanical Engineering Congress and Exposition, Montreal, QC, Canada, 14–20 November 2014; p. 46421.
13. Bani-Hani, D.; Khan, N.; Alsultan, F.; Karanjkar, S.; Nagarur, N. Classification of leucocytes using convolutional neural network optimized through genetic algorithm. In Proceedings of the 7th Annual World Conference of the Society for Industrial and Systems Engineering, Binghamton, NY, USA, 11–12 October 2018.
14. Zeidabadi, F.A.; Dehghani, M. Poa: Puzzle optimization algorithm. Int. J. Intell. Eng. Syst. 2022, 15, 273–281.
15. Anju, K.; Avanish, K. An advanced approach to the employee recruitment process through genetic algorithm. Int. J. Inf. Technol. 2021, 13, 313–319.
16. Begum, S.; Padmannavar, S.S. Genetically Optimized Ensemble Classifiers for Multiclass Student Performance Prediction. Int. J. Intell. Eng. Syst. 2022, 15, 316–328.
17. Haldulakar, R.; Agrawal, J. Optimization of association rule mining through genetic algorithm. Int. J. Comput. Sci. Eng. (IJCSE) 2011, 3, 1252–1259.
18. Muthana, S.A.; Ku-Mahamud, K.R. Comparison of Multi-objective Optimization Methods for Generator Maintenance Scheduling. Methods (Multi-Object. Metaheuristics) 2022, 14, 15.
19. Rabee, F.; Jazaery, I.; Kumar, K. Quaternary-Child Crossover for Genetic Algorithm in Real-Time Scheduling Optimization. Int. J. Intell. Eng. Syst. 2023, 16, 100–114.
20. Abdulkadhim, F.G.; Yi, Z.; Onaizah, A.N.; Rabee, F.; Al-Muqarm AM, A. Optimizing the Roadside Unit Deployment Mechanism in VANET with Efficient Protocol to Prevent Data Loss. Wirel. Pers. Commun. 2022, 127, 815–843.
21. Lu, F.; Bi, H.; Huang, M.; Duan, S. Simulated Annealing Genetic Algorithm Based Schedule Risk Management of IT Outsourcing Project. Math. Probl. Eng. 2017, 2017, 6916575.
22. Pandey, H.M.; Chaudhary, A.; Mehrotra, D. Grammar induction using bit masking oriented genetic algorithm and comparative analysis. Appl. Soft Comput. 2016, 38, 453–468.
23. Bi, H.; Lu, F.; Duan, S.; Huang, M.; Zhu, J.; Liu, M. Two-level principal–agent model for schedule risk control of IT outsourcing project based on genetic algorithm. Eng. Appl. Artif. Intell. 2020, 91, 103584.
24. Yan, T.; Lu, F.; Wang, S.; Wang, L.; Bi, H. A hybrid metaheuristic algorithm for the multi-objective location-routing problem in the early post-disaster stage. J. Ind. Manag. Optim. 2022, 19, 4663–4691.
25. Abdul-Sada, H.H.; Rabee, F. The Genetic Algorithm Implementation in Smart Contract for the Blockchain Technology. Al-Salam J. Eng. Technol. 2023, 2, 37–47.
26. Singh, J. An Optimal Resource Provisioning Scheme Using QoS in Cloud Computing Based Upon the Dynamic Clustering and Self-Adaptive Hybrid Optimization Algorithm. Int. J. Intell. Eng. Syst. 2022, 15, 148–160.
27. Sanapala, R.K.; Duggirala, S.R. An Optimized Energy Efficient Routing for Wireless Sensor Network using Improved Spider Monkey Optimization Algorithm. Transportation 2022, 8, 9.
28. Jubair, A.M.; Hassan, R.; Aman, A.H.; Sallehudin, H.; Al-Mekhlafi, Z.G.; Mohammed, B.A.; Alsaffar, M.S. Optimization of Clustering in Wireless Sensor Networks: Techniques and Protocols. Appl. Sci. 2021, 11, 11448.
29. Rangappa, M.H.; Dyamanna, G.C. Energy-Efficient Routing Protocol for Hybrid Wireless Sensor Networks Using Falcon Optimization Algorithm. Int. J. Intell. Eng. Syst. 2022, 15, 1–10.
30. Cinat, P.; Gnecco, G.; Paggi, M. Multi-scale surface roughness optimization through genetic algorithms. Front. Mech. Eng. 2020, 6, 29.
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