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What is Evolutionary Algorithms

Chapter 7
Metaheuristic Approaches to Portfolio Optimization
Evolutionary algorithms are the population-based metaheuristic optimization algorithms that are inspired by biological evolution.
Published in Chapter:
The Genetic Algorithm: An Application on Portfolio Optimization
Burcu Adıguzel Mercangöz (Istanbul University, Turkey) and Ergun Eroglu (Istanbul University, Turkey)
Copyright: © 2019 |Pages: 25
DOI: 10.4018/978-1-5225-8103-1.ch007
Abstract
The portfolio optimization is an important research field of the financial sciences. In portfolio optimization problems, it is aimed to create portfolios by giving the best return at a certain risk level from the asset pool or by selecting assets that give the lowest risk at a certain level of return. The diversity of the portfolio gives opportunity to increase the return by minimizing the risk. As a powerful alternative to the mathematical models, heuristics is used widely to solve the portfolio optimization problems. The genetic algorithm (GA) is a technique that is inspired by the biological evolution. While this book considers the heuristics methods for the portfolio optimization problems, this chapter will give the implementing steps of the GA clearly and apply this method to a portfolio optimization problem in a basic example.
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Chapter 16
A Comprehensive Review of Nature-Inspired Algorithms for Feature Selection
Systems made by writing an algorithm that generate a random set of solutions for the given problem which can be optimized by iteration.
Published in Chapter: A Comprehensive Review of Nature-Inspired Algorithms for Feature Selection; From: Handbook of Research on Modeling, Analysis, and Application of Nature-Inspired Metaheuristic Algorithms
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Chapter 2
An Intelligent Process Development Using Fusion of Genetic Algorithm with Fuzzy Logic
Evolutionary Algorithms are inspired by natural evolution. They are especially associated to Artificial Intelligence (AI) search techniques. EAs are computer programs that attempt to solve complex problems by mimicking the processes of Darwinian evolution. The evolutionary system performs evolutionary computing. Evolutionary computing (EC) is the basis of Evolutionary Algorithms (EA).
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 154
Many-Objective Evolutionary Optimisation
Solution methods inspired by the natural evolution process that evolve a population of solutions to an optimisation problem through iterative application of randomised processes of recombination and selection, until a termination criteria is met
Published in Chapter: Many-Objective Evolutionary Optimisation; From: Encyclopedia of Artificial Intelligence
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Chapter 15
Dot Net Platform for Distributed Evolutionary Algorithms with Application in Hydroinformatics
In artificial intelligence, an evolutionary algorithm is a subset of evolutionary computation, a generic population-based metaheuristic optimization algorithm.
Published in Chapter: Dot Net Platform for Distributed Evolutionary Algorithms with Application in Hydroinformatics; From: Handbook of Research on High Performance and Cloud Computing in Scientific Research and Education
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Chapter 233
Visualizing Cancer Databases Using Hybrid Spaces
A subset of evolutionary computation, which generally only involve techniques inspired by biological evolution such as reproduction, mutation, recombination, natural selection and survival of the fittest. Candidate solutions to an optimization problem play the role of individuals in a population, and the fitness function determines the environment within which the solutions “live”. Evolution of the population then takes place after the repeated application of the above operators.http://en.wikipedia.org/wiki/Evolutionary_Computation
Published in Chapter: Visualizing Cancer Databases Using Hybrid Spaces; From: Encyclopedia of Artificial Intelligence
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Chapter 117
GTM User Modeling for aIGA Weight Tuning in TTS Synthesis
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.
Published in Chapter: GTM User Modeling for aIGA Weight Tuning in TTS Synthesis; From: Encyclopedia of Artificial Intelligence
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Chapter 14
Cuckoo Search for Optimization and Computational Intelligence
A subset of optimization algorithms in evolutionary computation, mainly uses genetic operators such as crossover, mutation and selection. Genetic algorithms and evolutionary programming are good examples.
Published in Chapter: Cuckoo Search for Optimization and Computational Intelligence; From: Encyclopedia of Information Science and Technology, Third Edition
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Chapter 20
A Transfer Learning Approach for Smart Home Application Based on Evolutionary Algorithms
Is an algorithm that solves tasks by modeling the behavior of life creatures using natural mechanism.
Published in Chapter: A Transfer Learning Approach for Smart Home Application Based on Evolutionary Algorithms; From: Handbook of Research on AI Methods and Applications in Computer Engineering
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Chapter 3
An Artificial Immune Dynamical System for Optimization
Evolutionary algorithms are search methods that take their inspiration from natural selection and survival of the fittest in the biological world.
Published in Chapter: An Artificial Immune Dynamical System for Optimization; From: Handbook of Research on Artificial Immune Systems and Natural Computing: Applying Complex Adaptive Technologies
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Chapter 19
Ambiguity Reduction through Optimal Set of Region Selection Using GA and BFO for Handwritten Bangla Character Recognition
Evolutionary algorithm is used to describe the computational models of evolutionary processes which consists of rules of selection and other operators, such as recombination and mutation as key elements in their design and implementation. After initializing the first population structures, EA evaluates the population and used different selection, recombination, and mutation to generate new population. In this way it reaches towards the goal.
Published in Chapter: Ambiguity Reduction through Optimal Set of Region Selection Using GA and BFO for Handwritten Bangla Character Recognition; From: Handbook of Research on Swarm Intelligence in Engineering
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Chapter 43
Vapor Compression Refrigeration System Data-Based Comprehensive Model
Heuristic search methods inspired by evolution and living organisms to solving problems that cannot be easily work out in polynomial time.
Published in Chapter: Vapor Compression Refrigeration System Data-Based Comprehensive Model; From: Encyclopedia of Data Science and Machine Learning
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Chapter 3
Swarm Intelligence in Solving Bio-Inspired Computing Problems: Reviews, Perspectives, and Challenges
Systems made by writing an algorithm that generate a random set of solutions for the given problem which can be optimized by iteration.
Published in Chapter: Swarm Intelligence in Solving Bio-Inspired Computing Problems: Reviews, Perspectives, and Challenges; From: Handbook of Research on Swarm Intelligence in Engineering
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Chapter 11
Autonomic Networking
Optimization algorithms that imitate processes of life such as mutation, selection, and reproduction. Genetic algorithms, evolutionary programming, evolution strategies, classifier systems, and genetic programming are considered part of this category of optimization techniques.
Published in Chapter: Autonomic Networking; From: Encyclopedia of Internet Technologies and Applications
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Chapter 43
Composite Classifiers for Bankruptcy Prediction
Population-based optimization algorithms in which each member of the population represents a candidate solution. In an iterative process the population members evolve and are then evaluated by a fitness function. Genetic Algorithms and Particle Swarm Optimization are examples of evolutionary algorithms.
Published in Chapter: Composite Classifiers for Bankruptcy Prediction; From: Encyclopedia of Business Analytics and Optimization
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Chapter 88
Evolutionary Approaches for ANNs Design
Algorithms based on models that consider ‘artificial’ or ‘simulated’ genetic evolution of individuals in a defined environment. They are a broad class of stochastic optimization algorithms, inspired by biology and in particular by those biological processes that allow populations of organisms to adapt to their surrounding environment: genetic inheritance and survival of the fittest.
Published in Chapter: Evolutionary Approaches for ANNs Design; From: Encyclopedia of Artificial Intelligence
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