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What is Evolutionary Computation

Chapter 43
Handbook of Research on ICTs and Management Systems for Improving Efficiency in Healthcare and Social Care
Consists on adaptive robust methods with application in search, optimization and learning problems. As the name suggests, these methods were inspired by the principles of genetics and natural evolution of biological organisms.
Published in Chapter:
Soft Methods for Automatic Drug Infusion in Medical Care Environment
Filipe Quinaz (University of Beira Interior, Portugal), Paulo Fazendeiro (University of Beira Interior, Portugal & Portuguese Telecommunications Institute (IT), Portugal), Miguel Castelo-Branco (University of Beira Interior, Portugal), and Pedro Araújo (University of Beira Interior, Portugal & Portuguese Telecommunications Institute (IT), Portugal)
Copyright: © 2013 |Pages: 25
DOI: 10.4018/978-1-4666-3990-4.ch043
Abstract
The automatic drug infusion in medical care environment remains an elusive goal due to the inherent specificities of the biological systems under control and to subtle shortcomings of the current models. The central aim of this chapter is to present an overview of soft computing techniques and systems that can be used to ameliorate those problems. The applications of control systems in modern medicine are discussed along with several enabling methodologies. The advantages and limitations of automatic drug infusion systems are analyzed. In order to comprehend the evolution of these systems and identify recent advances and research trends, a survey on the hypertension control problem is provided. For illustration, a state-of-the-art automatic drug infusion controller of Sodium Nitroprusside for the mean arterial pressure is described in detail. The chapter ends with final remarks on future research directions towards a fully automated drug infusion system.
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Chapter 43
Soft Methods for Automatic Drug Infusion in Medical Care Environment
Consists on adaptive robust methods with application in search, optimization and learning problems. As the name suggests, these methods were inspired by the principles of genetics and natural evolution of biological organisms.
Published in Chapter: Soft Methods for Automatic Drug Infusion in Medical Care Environment; From: Handbook of Research on ICTs and Management Systems for Improving Efficiency in Healthcare and Social Care
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Chapter 91
Evolutionary Grammatical Inference
Large and diverse class of population-based search algorithms that is inspired by the process of biological evolution through selection, mutation and recombination. They are iterative algorithms that start with an initial population of candidate solutions and then repeatedly apply a series of the genetic operators
Published in Chapter: Evolutionary Grammatical Inference; From: Encyclopedia of Artificial Intelligence
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Chapter 12
Intelligence-Based Adaptive Digital Watermarking for Images in Wavelet Transform Domain
Evolutionary algorithms use a population of individuals, where an individual is referred to as a chromosome. A chromosome defines the characteristics of individuals in the population. Each characteristic is referred to as a gene.
Published in Chapter: Intelligence-Based Adaptive Digital Watermarking for Images in Wavelet Transform Domain; From: Handbook of Research on Emerging Perspectives in Intelligent Pattern Recognition, Analysis, and Image Processing
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Chapter 16
Studying Individualized Transit Indicators Using a New Low-Cost Information System
Subfield of artificial intelligence (more particularly computational intelligence) that involves continuous optimization and combinatorial optimization problems.
Published in Chapter: Studying Individualized Transit Indicators Using a New Low-Cost Information System; From: Handbook of Research on Embedded Systems Design
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Chapter 94
Evolving Graphs for ANN Development and Simplification
Set of Artificial Intelligence techniques used in optimization problems, which are inspired in biologic mechanisms such as natural evolution
Published in Chapter: Evolving Graphs for ANN Development and Simplification; From: Encyclopedia of Artificial Intelligence
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Chapter 588
Image Segmentation Methods
Subfield of computational intelligence that involves continuous optimization and combinatorial optimization problems and can be considered global optimization methods.
Published in Chapter: Image Segmentation Methods; From: Encyclopedia of Information Science and Technology, Third Edition
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Chapter 128
A Hybrid System for Automatic Infant Cry Recognition II
A subfield of computational intelligence that involves combinatorial optimization problems. It uses iterative progress, such as growth or development in a population, which is then selected in a guided random search to achieve the desired end. Such processes are often inspired by biological mechanisms of evolution
Published in Chapter: A Hybrid System for Automatic Infant Cry Recognition II; From: Encyclopedia of Artificial Intelligence
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Chapter 17
Particle Swarm Optimization Algorithm and its Hybrid Variants for Feature Subset Selection
Evolutionary computation is characterized by an iterative procedure leading to the solution of a guided random search process.
Published in Chapter: Particle Swarm Optimization Algorithm and its Hybrid Variants for Feature Subset Selection; From: Handbook of Research on Computational Intelligence for Engineering, Science, and Business
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Chapter 191
Particle Swarm Optimization and Image Analysis
Collection of techniques, basically aimed at function optimization but applicable to a huge variety of problems, by which the optimum of a function (fitness function) is sought through iterative refinements, according to rules inspired by the laws of natural evolution.
Published in Chapter: Particle Swarm Optimization and Image Analysis; From: Encyclopedia of Artificial Intelligence
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Chapter 115
Constructivist Apprenticeship through Antagonistic Programming Activities
It is the field of research that deals with the design, and application to engineering problems (e.g., optimization, learning), of bio-inspired algorithms that embody the quintessential characteristics of natural evolutionary systems.
Published in Chapter: Constructivist Apprenticeship through Antagonistic Programming Activities; From: Encyclopedia of Information Science and Technology, Second Edition
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Chapter 28
Evolutionary Multi-Objective Optimization of Autonomous Mobile Robots in Neural-Based Cognition for Behavioural Robustness
refers to a subfield of artificial intelligence or computational intelligence that involves computational algorithms that are inspired by biological processes.
Published in Chapter: Evolutionary Multi-Objective Optimization of Autonomous Mobile Robots in Neural-Based Cognition for Behavioural Robustness; From: Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques
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Chapter 19
ANN Development with EC Tools: An Overview
Set of Artificial Intelligence techniques used in optimization problems, which are inspired in biologic mechanisms such as natural evolution.
Published in Chapter: ANN Development with EC Tools: An Overview; From: Encyclopedia of Artificial Intelligence
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Chapter 17
Machine Learning and Financial Investing
In computer science evolutionary computation is a subfield of artificial intelligence (more particularly computational intelligence) that uses iterative progress in a population selected in a guided random search using parallel processing to achieve the desired end.
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 232
2D-PAGE Analysis Using Evolutionary Computation
Generic term used to indicate any population-based metaheuristic optimization algorithm that uses mechanisms inspired by biological evolution (Darwin, D., 1859) (Wallace, A. R., 1858), such as reproduction, mutation and recombination.
Published in Chapter: 2D-PAGE Analysis Using Evolutionary Computation; From: Encyclopedia of Artificial Intelligence
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Chapter 88
Evolutionary Approaches for ANNs Design
In computer science it is a subfield of artificial intelligence (more particularly computational intelligence) involving combinatorial optimization problems. Evolutionary computation defines the quite young field of the study of computational systems based on the idea of natural evolution and adaptation.
Published in Chapter: Evolutionary Approaches for ANNs Design; From: Encyclopedia of Artificial Intelligence
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Chapter 17
Design of Linear Phase FIR Low Pass Filter Using Mutation-Based Particle Swarm Optimization Technique
It refers to the collection of problem-solving approaches that are based on concept of biological evolution.
Published in Chapter: Design of Linear Phase FIR Low Pass Filter Using Mutation-Based Particle Swarm Optimization Technique; From: Applications of Artificial Intelligence in Electrical Engineering
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Chapter 74
Differential Evolution with Self-Adaptation
Solution approach guided by biological 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 model that best represents the data.
Published in Chapter: Differential Evolution with Self-Adaptation; From: Encyclopedia of Artificial Intelligence
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Chapter 16
Hybrid Computational Intelligence
Involves continuous and combinatorial optimization. Its algorithms can be considered global optimization methods of metaheuristic or stochastic nature, proper for solving black box problems.
Published in Chapter: Hybrid Computational Intelligence; From: Encyclopedia of Information Science and Technology, Third Edition
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Chapter 126
Hybrid Meta-Heuristics Based System for Dynamic Scheduling
A subfield of artificial intelligence that involve techniques implementing mechanisms inspired by biological evolution such as reproduction, mutation, recombination, natural selection and survival of the fittest.
Published in Chapter: Hybrid Meta-Heuristics Based System for Dynamic Scheduling; From: Encyclopedia of Artificial Intelligence
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Chapter 26
Artificial NeuroGlial Networks
Solution approach guided by biological 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 model that best represents the data.
Published in Chapter: Artificial NeuroGlial Networks; From: Encyclopedia of Artificial Intelligence
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Chapter 119
Harmony Search for Multiple Dam Scheduling
Solution approach guided by biological 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 model that best represents the data
Published in Chapter: Harmony Search for Multiple Dam Scheduling; From: Encyclopedia of Artificial Intelligence
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Chapter 15
Evolutionary Computing Approaches to System Identification
Evolutionary Computation is a stochastic optimization approach which provides a global optimal solution.
Published in Chapter: Evolutionary Computing Approaches to System Identification; From: Handbook of Research on Computational Intelligence Applications in Bioinformatics
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Chapter 166
Multilogistic Regression by Product Units
Computation based on iterative progress, such as growth or development in a population. This population is selected in a guided random search using parallel processing to achieve the desired solution. Such processes are often inspired by biological mechanisms of evolution.
Published in Chapter: Multilogistic Regression by Product Units; From: Encyclopedia of Artificial Intelligence
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