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What is Genetic Algorithms

Chapter 20
Handbook of Research on Natural Computing for Optimization Problems
Genetic algorithm (GA) is basically a heuristic process for mimicking the process of selection by nature via “survival of the fittest”. It therefore leads to productive inferences for minimization and optimization. Gas can be termes a part of the huge category of evolutionary algorithms (EA). It moreover, produces inferences for severe optimization issues utilizing the methodologies of evolution with the advancement in nature, like, crossover, mutation, selection and inheritance.
Published in Chapter:

Adaptive Simulated Annealing Algorithm to Solve Bio-Molecular Optimization

Sujay Ray (University of Kalyani, India)
Copyright: © 2016 | Pages: 15
DOI: 10.4018/978-1-5225-0058-2.ch020

Abstract

Energy minimization is a paramount zone in the field of computational and structural biology for protein modeling. It helps in mending distorted geometries in the folded functional protein by moving its atoms to release internal constraints. It attempts to hold back to zero value for the net atomic force on every atom. But to overcome certain disadvantages in energy minimization, Simulated Annealing (SA) can be helpful. SA is a molecular dynamics technique, where temperature is gradually reduced during the simulation. It provides the best configuration of bio-molecules in shorter time. With the advancement in computational knowledge, one essential but less sensitive variant of SA: Adaptive Simulated Annealing (ASA) algorithm is beneficial, because it automatically adjusts the temperature scheme and abrupt opting of step. Therefore it benefits to prepare stable protein models and further to investigate protein-protein interactions. Thus, a residue-level study can be analyzed in details for the benefit of the entire biota.
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Chapter 90
Evolutionary Computing Approach for Ad-Hoc Networks
The algorithms that are modelled on the natural process of evolution. These algorithms employ methods such as crossover, mutation and natural selection and provide the best possible solutions after analyzing a group of sub-optimal solutions which are provided as inputs
Published in Chapter: Evolutionary Computing Approach for Ad-Hoc Networks; From: Encyclopedia of Artificial Intelligence
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Chapter 70
Designing Unsupervised Hierarchical Fuzzy Logic Systems
Genetic Algorithms (GAs) are algorithms that use operations found in natural genetics to guide their way through a search space and are increasingly being used in the field of optimisation. The robust nature and simple mechanics of genetic algorithms make them inviting tools for search, learning and optimization. Genetic algorithms are based on computational models of fundamental evolutionary processes such as selection, recombination and mutation
Published in Chapter: Designing Unsupervised Hierarchical Fuzzy Logic Systems; From: Encyclopedia of Artificial Intelligence
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Chapter 15
Artificial Intelligence Techniques for Solar Energy and Photovoltaic Applications
Search algorithms used in machine learning which involve iteratively generating new candidate solutions by combining two high scoring earlier (or parent) solutions in a search for a better solution.
Published in Chapter: Artificial Intelligence Techniques for Solar Energy and Photovoltaic Applications; From: Handbook of Research on Solar Energy Systems and Technologies
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Chapter 128
A Hybrid System for Automatic Infant Cry Recognition II
A family of computational models inspired by evolution. These algorithms encode a potential solution to a specific problem on a simple chromosome-like data structure and apply recombination operators to these structures so as to preserve critical information. Genetic algorithms are often viewed as function optimizers, although the range of problems to which genetic algorithms have been applied is quite broad
Published in Chapter: A Hybrid System for Automatic Infant Cry Recognition II; From: Encyclopedia of Artificial Intelligence
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Chapter 2
An Intelligent Process Development Using Fusion of Genetic Algorithm with Fuzzy Logic
Algorithms based on principles of Darwinian evolution (natural evolution). They are successfully applied to the problems which are difficult to solve using conventional techniques. Machine learning and optimization effectively use Genetic Algorithms. They are basically search algorithms but can be applied to model learning tasks also. Robotics and swarm intelligent systems as well as evolutionary systems are taking advantages of parallel processing and multi-objective optimization due to characteristics of Genetic Algorithms. Genetic Algorithms are widely used in engineering, scientific and business applications.
Published in Chapter: An Intelligent Process Development Using Fusion of Genetic Algorithm with Fuzzy Logic; From: Handbook of Research on Artificial Intelligence Techniques and Algorithms
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Chapter 14
Application of Artificial Intelligence Techniques to Handle the Uncertainty in the Chemical Process for Environmental Protection
In the field of artificial intelligence, a genetic algorithm (GA) is a search heuristic with the process of natural selection.
Published in Chapter: Application of Artificial Intelligence Techniques to Handle the Uncertainty in the Chemical Process for Environmental Protection; From: Handbook of Research on Artificial Intelligence Techniques and Algorithms
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Chapter 12
Artificial Intelligence and Supply Chain Management Application, Development, and Forecast
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Chapter 7
Conclusion: Re-Coding Homes Project
A computational and generative design approach which is used to solve non-lineer design problems.
Published in Chapter: Conclusion: Re-Coding Homes Project; From: Re-Coding Homes Through Flexible Interiors: Emerging Research and Opportunities
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Chapter 180
Neural Networks on Handwritten Signature Verification
A genetic algorithm is technique used for searching or programming. It is used in computing to find true or approximate solutions to optimization and search problems of various types and used as a function in evolutionary computation. Genetic algorithms are based on biological events. They mimic biological evolution.
Published in Chapter: Neural Networks on Handwritten Signature Verification; From: Encyclopedia of Artificial Intelligence
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Chapter 519
Biogeography-Based Optimization Applied to Wireless Communications Problems
A stochastic population-based global optimization technique that mimics the process of natural evolution.
Published in Chapter: Biogeography-Based Optimization Applied to Wireless Communications Problems; From: Encyclopedia of Information Science and Technology, Fourth Edition
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Chapter 48
Designing Multilayer Feedforward Neural Networks Using Multi-Verse Optimizer
A bio-inspired algorithm that imitates the behavior of surviving individual to optimize solutions through mutation, crossover, and selection operations.
Published in Chapter: Designing Multilayer Feedforward Neural Networks Using Multi-Verse Optimizer; From: Handbook of Research on Machine Learning Innovations and Trends
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Chapter 17
Machine Learning and Financial Investing
Genetic algorithms are search procedures based on the mechanics of natural selection and genetics and are in the class of evolutionary computation techniques.
Published in Chapter: Machine Learning and Financial Investing; From: Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques
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Chapter 18
A Dynamic Subspace Anomaly Detection Method Using Generic Algorithm for Streaming Network Data
A search heuristic that mimics the process of natural evolution. This heuristic is routinely used to generate useful solutions to optimization and search problems.
Published in Chapter: A Dynamic Subspace Anomaly Detection Method Using Generic Algorithm for Streaming Network Data; From: Handbook of Research on Emerging Developments in Data Privacy
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Chapter 6
Advanced Methodologies Descriptions and Applications
A computer program that is based upon the principles of genetic evolution and is effective at searching for solutions to complex problems. Also called evolutionary computing.
Published in Chapter: Advanced Methodologies Descriptions and Applications; From: Encyclopedia of Business Analytics and Optimization
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Chapter 26
Optimization of Antenna Design Problems Using Binary Differential Evolution
A stochastic population-based global optimization technique that mimics the process of natural evolution.
Published in Chapter: Optimization of Antenna Design Problems Using Binary Differential Evolution; From: Handbook of Research on Emergent Applications of Optimization Algorithms
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Chapter 192
Real-Time Smart Navigation and the Genetic Approach to Vehicle Routing
Heuristic-based search algorithms that “imitate” the process of natural selection.
Published in Chapter: Real-Time Smart Navigation and the Genetic Approach to Vehicle Routing; From: Encyclopedia of Information Science and Technology, Third Edition
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Chapter 95
Intelligent Personalization Agent for Product Brokering
Inspired by evolutionary biology, genetic algorithms are a particular class of evolutionary algorithms that use techniques such as inheritance, mutation, selection, and crossover in problem solving
Published in Chapter: Intelligent Personalization Agent for Product Brokering; From: Encyclopedia of Multimedia Technology and Networking, Second Edition
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Chapter 22
An Exploration of Backpropagation Numerical Algorithms in Modeling US Exchange Rates
A heuristic search algorithm that mimics the process of natural selection used for optimization purpose using mutation, selection, and crossover operators.
Published in Chapter: An Exploration of Backpropagation Numerical Algorithms in Modeling US Exchange Rates; From: Handbook of Research on Organizational Transformations through Big Data Analytics
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Chapter 20
A Hybrid GA-GSA Algorithm for Optimizing the Performance of an Industrial System by Utilizing Uncertain Data
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Chapter 568
Self-Adaptive Differential Evolution Algorithms for Wireless Communications and the Antenna and Microwave Design Problems
A stochastic population-based global optimization technique that mimics the process of natural evolution.
Published in Chapter: Self-Adaptive Differential Evolution Algorithms for Wireless Communications and the Antenna and Microwave Design Problems; From: Encyclopedia of Information Science and Technology, Third Edition
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Chapter 221
Supervised Learning of Fuzzy Logic Systems
Genetic Algorithms (GAs) are algorithms that use operations found in natural genetics to guide their way through a search space and are increasingly being used in the field of optimisation. The robust nature and simple mechanics of genetic algorithms make them inviting tools for search learning and optimization. Genetic algorithms are based on computational models of fundamental evolutionary processes such as selection, recombination and mutation
Published in Chapter: Supervised Learning of Fuzzy Logic Systems; From: Encyclopedia of Artificial Intelligence
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Chapter 7
Population-Based vs. Single Point Search Meta-Heuristics for a PID Controller Tuning
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Chapter 149
Data Mining in Tourism
These algorithms mimic the process of natural evolution and perform explorative search. The main component of this method is chromosomes that represent solutions to the problem. It uses selection, crossover, and mutation to obtain chromosomes of highest quality.
Published in Chapter: Data Mining in Tourism; From: Encyclopedia of Information Science and Technology, Second Edition
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Chapter 2
Impact of Artificial Intelligence on Marketing Research: Challenges and Ethical Considerations
A type of AI system that uses principles of natural selection and genetics to optimise solutions to complex problems.
Published in Chapter: Impact of Artificial Intelligence on Marketing Research: Challenges and Ethical Considerations; From: Philosophy of Artificial Intelligence and Its Place in Society
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Chapter 5
Home Load-Side Management in Smart Grids Using Global Optimization
A metaheuristic inspired from the process of natural selection and are used to produce high-quality solutions to optimization and search problems by relying on bio-inspired operators such as mutation, crossover, and selection.
Published in Chapter: Home Load-Side Management in Smart Grids Using Global Optimization; From: Handbook of Research on Smart Power System Operation and Control
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Chapter 1
Re-Coding Homes: A Mass Customization Tool to Create Flexibility for Housing Units
A computational and generative design approach, which is used to solve non-linear design problems.
Published in Chapter: Re-Coding Homes: A Mass Customization Tool to Create Flexibility for Housing Units; From: Re-Coding Homes Through Flexible Interiors: Emerging Research and Opportunities
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Chapter 26
Artificial NeuroGlial Networks
Genetic algorithms (GAs) are adaptive heuristic search algorithm premised on the evolutionary ideas of natural selection and genetic. The basic concept of GAs is designed to simulate processes in natural system necessary for evolution, specifically those that follow the principles first laid down by Charles Darwin of survival of the fittest. As such they represent an intelligent exploitation of a random search within a defined search space to solve a problem.
Published in Chapter: Artificial NeuroGlial Networks; From: Encyclopedia of Artificial Intelligence
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Chapter 7
Applications of Data Mining Techniques in Smart Farming for Sustainable Agriculture
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Chapter 126
Hybrid Meta-Heuristics Based System for Dynamic Scheduling
Particular class of evolutionary algorithms that use techniques inspired by evolutionary biology such as inheritance, mutation, selection, and crossover.
Published in Chapter: Hybrid Meta-Heuristics Based System for Dynamic Scheduling; From: Encyclopedia of Artificial Intelligence
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Chapter 6
Development of the User Interface: Re-Coding Homes With User Participation
A computational and generative design approach which is used to solve non-lineer design problems.
Published in Chapter: Development of the User Interface: Re-Coding Homes With User Participation; From: Re-Coding Homes Through Flexible Interiors: Emerging Research and Opportunities
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Chapter 4
Different Design Approaches for Well-Being
A generative design method inspired by the evolutionary process of nature that simulate a long-time natural selection in a short time.
Published in Chapter: Different Design Approaches for Well-Being; From: Well-Being Design and Frameworks for Interior Space
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Chapter 5
Diagnostic Support Systems and Computational Intelligence: Differential Diagnosis of Hepatic Lesions from Computed Tomography Images
Algorithms which vary a set of parameters and evaluate the quality or “fitness” of the results of a computation as the parameters are changed or “evolved”.
Published in Chapter: Diagnostic Support Systems and Computational Intelligence: Differential Diagnosis of Hepatic Lesions from Computed Tomography Images; From: Handbook of Research on Advanced Techniques in Diagnostic Imaging and Biomedical Applications
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Chapter 6
Computational Models for the Analysis of Modern Biological Data
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Chapter 111
Genetic Algorithm Applications to Optimization Modeling
A stochastic search method which applies genetic operators to a population of solutions for progressively generating optimal or near-optimal solutions
Published in Chapter: Genetic Algorithm Applications to Optimization Modeling; From: Encyclopedia of Artificial Intelligence
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Chapter 9
Genetic Algorithms for Small Enterprises Default Prediction: Empirical Evidence from Italy
Genetic algorithms (GAs) is a stochastic search methodology belonging to the larger family of artificial intelligence procedures and evolutionary algorithms (EA). They are used to generate useful solutions to optimization and search problems mimicking Darwinian evolution.
Published in Chapter: Genetic Algorithms for Small Enterprises Default Prediction: Empirical Evidence from Italy; From: Handbook of Research on Novel Soft Computing Intelligent Algorithms: Theory and Practical Applications
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Chapter 62
Intelligent User Preference Mining
Search technique used in computer science to find approximate solutions to optimization and search problems. Genetic algorithms are a particular class of evolutionary algorithm that uses techniques inspired by evolutionary biology such as inheritance, mutation, natural selection, and recombination (or crossover).
Published in Chapter: Intelligent User Preference Mining; From: Encyclopedia of Information Communication Technology
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Chapter 66
Application of Biogeography-Based Optimization to Antennas and Wireless Communications
A stochastic population-based global optimization technique that mimics the process of natural evolution.
Published in Chapter: Application of Biogeography-Based Optimization to Antennas and Wireless Communications; From: Encyclopedia of Information Science and Technology, Fifth Edition
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Chapter 238
Watermarking Using Intelligent Methods: Survey
A genetic algorithm is an experience based technique which uses fuzzy logic for solving problems.
Published in Chapter: Watermarking Using Intelligent Methods: Survey; From: Encyclopedia of Business Analytics and Optimization
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Chapter 3
The Project Site: Survey Studies and Evaluation of Findings
A computational and generative design approach which is used to solve non-lineer design problems.
Published in Chapter: The Project Site: Survey Studies and Evaluation of Findings; From: Re-Coding Homes Through Flexible Interiors: Emerging Research and Opportunities
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Chapter 46
Current Issues and Future Trends of Clinical Decision Support Systems (CDSS)
A genetic algorithm is a search method used in computational intelligence to find true or approximate solutions to optimization and search problems.
Published in Chapter: Current Issues and Future Trends of Clinical Decision Support Systems (CDSS); From: Encyclopedia of Healthcare Information Systems
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Chapter 21
Interactive Personalized Catalogue for M-Commerce
Are methods used in computer science, engineering, and other fields to search for optimal solutions to optimization problems. Genetic algorithms use techniques inspired by evolutionary biology such as mutation, selection, and crossover.
Published in Chapter: Interactive Personalized Catalogue for M-Commerce; From: Handbook of Research on Public Information Technology
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Chapter 4
Genetic Algorithms: Stages of the Study in the Expert System
A computational and generative design approach which is used to solve non-lineer design problems.
Published in Chapter: Genetic Algorithms: Stages of the Study in the Expert System; From: Re-Coding Homes Through Flexible Interiors: Emerging Research and Opportunities
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Chapter 17
Navigation Control of a Mobile Robot under Time Constraint using Genetic Algorithms, CSP Techniques, and Fuzzy Logic
A search heuristic that mimics the process of natural selection. This heuristic is used to generate useful solutions to optimization and search problems.
Published in Chapter: Navigation Control of a Mobile Robot under Time Constraint using Genetic Algorithms, CSP Techniques, and Fuzzy Logic; From: Handbook of Research on Advanced Intelligent Control Engineering and Automation
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Chapter 112
Genetic Algorithms for Wireless Sensor Networks
Search technique used in computing to find true or approximate solutions to optimization and search problems.
Published in Chapter: Genetic Algorithms for Wireless Sensor Networks; From: Encyclopedia of Artificial Intelligence
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Chapter 572
Optimization of Antenna Arrays and Microwave Filters Using Differential Evolution Algorithms
A stochastic population-based global optimization technique that mimics the process of natural evolution.
Published in Chapter: Optimization of Antenna Arrays and Microwave Filters Using Differential Evolution Algorithms; From: Encyclopedia of Information Science and Technology, Fourth Edition
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Chapter 5
Novel Meta-Heuristic Optimization Techniques for Solving Fuzzy Programming Problems
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