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What is Evolutionary Algorithm (EA)

Chapter 48
Encyclopedia of Internet Technologies and Applications
Evolutionary algorithms (EA) are inspired by Darwin’s theory of evolution, which is based on the survival of the fittest species. The character of a life is determined by the chromosomes. In a chromosome there are many different genes, which indicate different characters. The EA principle is based on the different combinations of genes in a chromosome. Different combinations will lead to different characters and the more suitable ones will remain in the world.
Published in Chapter:
Network Optimization Using Evolutionary Algorithms in Multicast Transmission
Yezid Donoso (Universidad del Norte, Colombia) and Ramón Fabregat (Girona University, Spain)
Copyright: © 2008 |Pages: 7
DOI: 10.4018/978-1-59140-993-9.ch048
Abstract
To support QoS in today’s Internet, several new architecture models have been proposed (Striegel, A., & Manimaran, G. (2002)). Traffic engineering has become a key issue within these new architectures, as supporting QoS requires more sophisticated resource management tools. Traffic engineering aims to optimize the performance of operational networks. The main objective is to reduce congestion hot spots and improve resource utilization. This can be achieved by setting up explicit routes through the physical network in such a way that the traffic distribution is balanced across several traffic trunks. This load balancing technique can be achieved by multicommodity network flow (Pioro, M., & Medhi, D. (2004)) formulation. This leads to the traffic being shared over multiple routes between the ingress node and the egress nodes in order to avoid link saturation and hence the possibility of congestion, which is the inability to transmit a volume of information with the established capacities for a particular equipment or network.
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More Results
Chapter 14
Predicting Uncertain Behavior and Performance Analysis of the Pulping System in a Paper Industry using PSO and Fuzzy Methodology
A collective term for all variants of (probabilistic) optimization and approximation algorithms that are inspired by Darwinian evolution. Optimal states are approximated by successive improvements based on the variation-selectionparadigm. Thereby, the variation operators produce genetic diversity and the selection directs the evolutionary search.
Published in Chapter: Predicting Uncertain Behavior and Performance Analysis of the Pulping System in a Paper Industry using PSO and Fuzzy Methodology; From: Handbook of Research on Novel Soft Computing Intelligent Algorithms: Theory and Practical Applications
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Chapter 79
EA Multi-Model Selection for SVM
Meta-heuristic optimization approach inspired by natural evolution, which begins with potential solution models, then iteratively applies algorithms to find the fittest models from the set to serve as inputs to the next iteration, ultimately leading to a sub-optimal solution which is close to the optimal one.
Published in Chapter: EA Multi-Model Selection for SVM; From: Encyclopedia of Artificial Intelligence
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Chapter 63
Continuous ACO in a SVR Traffic Forecasting Model
is a generic population-based meta-heuristic optimization algorithm. An EA uses some mechanisms inspired by biological evolution: reproduction, mutation, recombination, natural selection and survival of the fittest. Evolutionary algorithms consistently perform well approximating solutions to all types of problems because they do not make any assumption about the underlying fitness landscape.
Published in Chapter: Continuous ACO in a SVR Traffic Forecasting Model; From: Encyclopedia of Artificial Intelligence
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Chapter 20
A Hybrid GA-GSA Algorithm for Optimizing the Performance of an Industrial System by Utilizing Uncertain Data
A collective term for all variants of (probabilistic) optimization and approximation algorithms that are inspired by Darwinian evolution. Optimal states are approximated by successive improvements based on the variation-selection paradigm. Thereby, the variation operators produce genetic diversity and the selection directs the evolutionary search.
Published in Chapter: A Hybrid GA-GSA Algorithm for Optimizing the Performance of an Industrial System by Utilizing Uncertain Data; From: Handbook of Research on Artificial Intelligence Techniques and Algorithms
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Chapter 25
Bi-Criteria Optimization for Finding the Optimal Replacement Interval for Maintaining the Performance of the Process Industries
A collective term for all variants of (probabilistic) optimization and approximation algorithms that are inspired by Darwinian evolution. Optimal states are approximated by successive improvements based on the variation-selection paradigm. Thereby, the variation operators produce genetic diversity and the selection directs the evolutionary search.
Published in Chapter: Bi-Criteria Optimization for Finding the Optimal Replacement Interval for Maintaining the Performance of the Process Industries; From: Handbook of Research on Modern Optimization Algorithms and Applications in Engineering and Economics
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