Optimization Approaches For Solving String Selection Problems
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Optimization Approaches for Solving String Selection Problems
Author: Elisa Pappalardo
language: en
Publisher: Springer Science & Business Media
Release Date: 2013-10-09
Optimization Approaches for Solving String Selection Problems provides an overview of optimization methods for a wide class of genomics-related problems in relation to the string selection problems. This class of problems addresses the recognition of similar characteristics or differences within biological sequences. Specifically, this book considers a large class of problems, ranging from the closest string and substring problems, to the farthest string and substring problems, to the far from most string problem. Each problem includes a detailed description, highlighting both biological and mathematical features and presents state-of-the-art approaches. This Brief provides a quick introduction of optimization methods for string selection problems for young scientists and a detailed description of the mathematical and computational methods developed for experts in the field of optimization who want to deepen their understanding of the string selection problems. Researchers, practitioners and graduate students in the field of Computer Science, Operation Research, Mathematics, Computational Biology and Biomedicine will find this book useful.
Metaheuristics for String Problems in Bio-informatics
So-called string problems are abundant in bioinformatics and computational biology. New optimization problems dealing with DNA or protein sequences are constantly arising and researchers are highly in need of efficient optimization techniques for solving them. One obstacle for optimization practitioners is the atypical nature of these problems which require an interdisciplinary approach in order to solve them efficiently and accurately.
Genetic AI Algorithms: Evolutionary Approaches for Solving Complex Computational Problems
Author: Rajesh Ojha Prof (Dr) Ajay Shriram Kushwaha
language: en
Publisher: DeepMisti Publication
Release Date: 2025-01-14
In an era where technology evolves at an unprecedented pace, the demand for efficient, adaptable, and innovative solutions to complex computational problems has never been greater. Traditional algorithms often struggle to tackle the complexity, non-linearity, and scale of challenges faced in various fields, ranging from artificial intelligence (AI) to data science, bioinformatics, and beyond. This is where the power of genetic algorithms (GAs) and other evolutionary computation techniques comes into play, offering a new paradigm for problem-solving inspired by the process of natural selection. Genetic AI Algorithms: Evolutionary Approaches for Solving Complex Computational Problems explores the fascinating intersection of evolutionary biology and computational intelligence. It delves into the principles, techniques, and applications of genetic algorithms (GAs), genetic programming (GP), and other evolutionary strategies to provide readers with a comprehensive understanding of how these methods can be used to address some of the most challenging problems in modern computing. Evolutionary algorithms draw inspiration from the mechanisms of natural evolution, such as selection, mutation, crossover, and inheritance. These methods excel at finding optimal or near-optimal solutions in vast, poorly understood, or highly complex problem spaces. By mimicking the evolutionary process, they can explore potential solutions in ways that are often more robust and flexible than traditional approaches. Whether it’s solving optimization problems, designing neural networks, evolving game strategies, or simulating biological systems, evolutionary algorithms provide a powerful framework for innovation. This book serves as both an introduction and a practical guide for those seeking to harness the power of genetic AI algorithms. It begins with foundational concepts and gradually builds up to more advanced topics, ensuring accessibility for newcomers while providing in-depth insights for experienced practitioners. Through a combination of theory, examples, and case studies, readers will learn how to apply evolutionary algorithms to real-world problems, gain insights into the latest research, and discover new frontiers in computational intelligence. By the end of this journey, readers will be equipped with the knowledge and tools necessary to implement genetic AI algorithms for solving a wide array of complex computational challenges. As you embark on this exploration, I encourage you to think creatively and embrace the potential of evolutionary approaches to drive progress in your work, whether in academia, industry, or any other domain where computational problems abound. In closing, it is my hope that this book inspires further inquiry and discovery in the exciting field of genetic AI algorithms, and that it provides a solid foundation for those seeking to unlock the full potential of evolutionary computation. Authors