Exploring Genetic Algorithms for Smart Solutions

Jessica Malone^*
*Correspondence: Jessica Malone, Department of Pharmacy Sciences, Creighton University, Omaha, USA, Email:

Introduction

Genetic algorithms are a type of evolutionary algorithm used for optimization and search problems. They draw inspiration from the process of natural selection, where the fittest individuals have a higher chance of passing on their traits to the next generation. In the realm of computing, GAs simulates this process to find solutions to complex problems. This article aims to provide an overview of genetic algorithms, their working principles, and how they can be applied to various real-world problems. Genetic Algorithms (GAs) are a powerful class of optimization techniques inspired by the principles of natural selection and genetics. They have found applications in a wide range of fields, from engineering and computer science to finance and biology [1].

Description

In genetic algorithms, a potential solution to a problem is represented as a chromosome, which consists of genes. These genes encode the characteristics or parameters of the solution. The arrangement of genes in a chromosome plays a crucial role in determining its fitness. A population is a collection of chromosomes. Initially, a random population is generated, and over successive generations, individuals with higher fitness are favoured. The fitness function evaluates how close a solution is to the desired outcome. It assigns a fitness score to each chromosome, guiding the selection process. Individuals with higher fitness scores have a better chance of being selected for reproduction. Various selection methods, such as roulette wheel selection and tournament selection, are used to choose parents for the next generation [2,3].

Crossover involves swapping genes between two parent chromosomes to create one or more offspring. This mimics the genetic recombination process in nature. Mutation introduces small random changes in the offspring's genes. This adds diversity to the population and helps avoid getting stuck in local optima. Genetic algorithms operate on the principle of survival of the fittest. The algorithm progresses through generations, with each generation improving upon the previous one. Evaluation is the fitness function is applied to each individual in the population, assigning a fitness score. Individuals are selected to become parents for the next generation, with a higher probability of selection for those with better fitness scores. Pairs of parents are selected and their genetic material is recombined to create one or more offspring [4].

Genetic algorithms have been applied successfully to numerous realworld problems. Some notable applications include: GAs are used to optimize complex functions and find the best parameters for a given problem, such as optimizing the design of structures, vehicle routing, and financial portfolio management. Genetic algorithms can be used to optimize machine learning models' hyper parameters, helping achieve better model performance. Game playing has been used in evolving strategies for playing games, such as chess and poker, to improve performance. GAs is employed to solve scheduling problems, such as employee scheduling, where finding an optimal solution can be highly challenging. In robotics, GAs are used for tasks like robot path planning and control [5].

Conclusion

Genetic algorithms offer a versatile and powerful approach to solving complex optimization and search problems. Their ability to evolve solutions over successive generations makes them applicable to a wide range of domains. By mimicking the principles of natural selection and genetics, GAs has been employed to tackle problems that were once considered computationally infeasible. As technology continues to advance, the capabilities of genetic algorithms are likely to expand, enabling them to address even more complex and diverse problem sets. Whether you are an engineer, data scientist, or researcher, understanding the basics of genetic algorithms can be a valuable addition to your problem-solving toolkit. As we explore and apply genetic algorithms to various challenges, we unlock new possibilities for finding smart solutions to the problems of today and tomorrow.

Acknowledgement

None.

Conflict of Interest

There are no conflicts of interest by author.

References

1. Lau, Hui-Lane, Fadzlie Wong Faizal Wong, Raja Noor Zaliha Raja Abd Rahman and Mohd Shamzi Mohamed, et al. "Optimization of fermentation medium components by Response Surface Methodology (RSM) and Artificial Neural Network hybrid with Genetic Algorithm (ANN-GA) for lipase production by Burkholderia cenocepacia ST8 using used automotive engine oil as substrate." Biocatal Agric Biotechnol 50 (2023): 102696.

Google Scholar, Crossref

2. Chen, Haoying, Bin Wang, Jinpeng Li and Jun Xu, et al. "Comparative study on the extraction efficiency, characterization, and bioactivities of Bletilla striata polysaccharides using Response Surface Methodology (RSM) and Genetic Algorithm-Artificial Neural Network (GA-ANN)." Int J Biol Macromol 226 (2023): 982-995.

Google Scholar, Crossref, Indexed at

3. Etemad, Alireza, Ali Shafaat and Ammar M. Bahman. "Data-driven performance analysis of a residential building applying Artificial Neural Network (ANN) and multi-objective Genetic Algorithm (GA)." Build Environ 225 (2022): 109633.

Google Scholar, Crossref, Indexed at

4. Amiri, Mostafa and Farzad Hatami. "Prediction of mechanical and durability characteristics of concrete including slag and recycled aggregate concrete with Artificial Neural Networks (ANNs)." Constr Build Mater 325 (2022): 126839.

Google Scholar, Crossref

5. Moein, Mohammad Mohtasham, Ashkan Saradar, Komeil Rahmati and Seyed Hosein Ghasemzadeh Mousavinejad, et al. "Predictive models for concrete properties using machine learning and deep learning approaches: A review." J Build Eng (2022): 105444.

Google Scholar, Crossref, Indexed at