Open access peer-reviewed Edited Volume

Genetic Algorithms

Sebastian Ventura Soto

University of Córdoba

A researcher in Artificial Intelligence and Computing Sciences, Dr. Ventura has authored more than ten books and over 300 articles in journals and scientific conferences(H-index of 37)and is a holder of a patent "Remote Control System for People with Disabilities". He is the head of the KDIS research group and a Senior Member of IEEE Computer, the IEEE Computational Intelligence, and the IEEE Systems, Man, and Cybernetics Society, and the ACM.


José Luna

University of Córdoba

A pioneering researcher in the use of Evolutionary Computation on Pattern Mining, Dr. Luna obtained his Ph.D. with a grade of summa cum laude in Computer Science and with a grant from the Spanish Ministry of Education. For his exceptional research efforts, Dr. Luna has also been awarded a JdC - training PostDoc grant financed by the Ministry of economy and competitiveness (Government of Spain).

Jose María Moyano

National University of Córdoba

An enthusiastic researcher in Machine Learning and Evolutionary Computation Models whose doctoral thesis was developed enrolled in a dual Ph.D. program at both University of Córdoba (Spain) and Virginia Commonwealth University (USA), and supervised by Eva L. Gibaja, Krzysztof J. Cios, and Sebastián Ventura. Dr. Moyano is a post-doctoral researcher at the KDIS Research Group of the University of Córdoba.


Evolutionary Algorithms Smart Mobility Smart Cities Data Mining Machine Learning Hyper-Parameter Optimization Architecture Optimization Data Preprocessing Pattern Mining Feature Selection Genetic Operators Genetic Encoding

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About the book

The solution to many real-world problems lies in optimizing processes, parameters, or techniques, among many others. However, these optimizations usually mean dealing with immense search spaces, so exhaustive methods that may evaluate all possible solutions looking for the global optimum are intractable. Besides, many local optima may exist in the search space, so simple techniques may get stuck in them. Evolutionary algorithms and, more concrete, genetic algorithms are metaheuristic techniques inspired by Darwin's natural selection of species theory to solve search-based optimization problems, which has been demonstrated to effectively deal with complex search spaces. Genetic algorithms employ a population of individuals, each representing a full or partial solution to the problem, bred and reproduced looking for optimal individuals. Then, according to a fitness function, these individuals are evaluated, which determines how a given individual adapts to the problem at hand.

In recent years, genetic algorithms have advanced by proposing novel algorithmic flows, representations, or specific techniques inside the main structure of the algorithm. As a result, genetic algorithms have been successfully applied to solve many real-world problems (engineering, smart cities, and energy). They have also helped to improve many machine learning (classification, regression, or hyperparameter optimization) and data mining (data preprocessing, pattern mining, or feature selection) techniques.

This book intends to provide the reader with a comprehensive overview of the current state-of-the-art and advances in genetic algorithms and present the fields in which they have been applied throughout the years.

Publishing process

Book initiated and editor appointed

Date completed: August 25th 2021

Applications to edit the book are assessed and a suitable editor is selected, at which point the process begins.

Chapter proposals submitted and reviewed

Deadline Extended: Open for Submissions

Potential authors submit chapter proposals ready for review by the academic editor and our publishing review team.

Approved chapters written in full and submitted

Deadline for full chapters: November 21st 2021

Once approved by the academic editor and publishing review team, chapters are written and submitted according to pre-agreed parameters

Full chapters peer reviewed

Review results due: February 9th 2022

Full chapter manuscripts are screened for plagiarism and undergo a Main Editor Peer Review. Results are sent to authors within 30 days of submission, with suggestions for rounds of revisions.

Book compiled, published and promoted

Expected publication date: April 10th 2022

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About the editor

Sebastian Ventura Soto

University of Córdoba

Sebastian Ventura is a Spanish researcher, a full professor with the Department of Computer Science and Numerical Analysis, University of Córdoba. Dr Ventura also holds the positions of Affiliated Professor at Virginia Commonwealth University (Richmond, USA) and Distinguished Adjunct Professor at King Abdulaziz University (Jeddah, Saudi Arabia). Additionally, he is deputy director of the Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) and heads the Knowledge Discovery and Intelligent Systems Research Laboratory. He has published more than ten books and over 300 articles in journals and scientific conferences. Currently, his work has received over 18,000 citations according to Google Scholar, including more than 2200 citations in 2020. In the last five years, he has published more than 60 papers in international journals indexed in the JCR (around 70% of them belonging to first quartile journals) and he has edited some Springer books “Supervised Descriptive Pattern Mining” (2018), “Multiple Instance Learning - Foundations and Algorithms” (2016), and “Pattern Mining with Evolutionary Algorithms” (2016). He has also been involved in more than 20 research projects supported by the Spanish and Andalusian governments and the European Union. He currently belongs to the editorial board of PeerJ Computer Science, Information Fusion and Engineering Applications of Artificial Intelligence journals, being also associate editor of Applied Computational Intelligence and Soft Computing and IEEE Transactions on Cybernetics. Finally, he is editor-in-chief of Progress in Artificial Intelligence. He is a Senior Member of the IEEE Computer, the IEEE Computational Intelligence, and the IEEE Systems, Man, and Cybernetics Societies, and the Association of Computing Machinery (ACM). Finally, his main research interests include data science, computational intelligence, and their applications.

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