Self Adaptive Heuristics For Evolutionary Computation
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Self Adaptive Heuristics For Evolutionary Computation
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Author : Oliver Kramer
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-08-19
Self Adaptive Heuristics For Evolutionary Computation written by Oliver Kramer and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-08-19 with Computers categories.
Evolutionary algorithms are successful biologically inspired meta-heuristics. Their success depends on adequate parameter settings. The question arises: how can evolutionary algorithms learn parameters automatically during the optimization? Evolution strategies gave an answer decades ago: self-adaptation. Their self-adaptive mutation control turned out to be exceptionally successful. But nevertheless self-adaptation has not achieved the attention it deserves. This book introduces various types of self-adaptive parameters for evolutionary computation. Biased mutation for evolution strategies is useful for constrained search spaces. Self-adaptive inversion mutation accelerates the search on combinatorial TSP-like problems. After the analysis of self-adaptive crossover operators the book concentrates on premature convergence of self-adaptive mutation control at the constraint boundary. Besides extensive experiments, statistical tests and some theoretical investigations enrich the analysis of the proposed concepts.
Adaptive And Multilevel Metaheuristics
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Author : Carlos Cotta
language : en
Publisher: Springer
Release Date : 2008-06-17
Adaptive And Multilevel Metaheuristics written by Carlos Cotta and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-06-17 with Technology & Engineering categories.
One of the keystones in practical metaheuristic problem-solving is the fact that tuning the optimization technique to the problem under consideration is crucial for achieving top performance. This tuning/customization is usually in the hands of the algorithm designer, and despite some methodological attempts, it largely remains a scientific art. Transferring a part of this customization effort to the algorithm itself -endowing it with smart mechanisms to self-adapt to the problem- has been a long pursued goal in the field of metaheuristics. These mechanisms can involve different aspects of the algorithm, such as for example, self-adjusting the parameters, self-adapting the functioning of internal components, evolving search strategies, etc. Recently, the idea of hyperheuristics, i.e., using a metaheuristic layer for adapting the search by selectively using different low-level heuristics, has also been gaining popularity. This volume presents recent advances in the area of adaptativeness in metaheuristic optimization, including up-to-date reviews of hyperheuristics and self-adaptation in evolutionary algorithms, as well as cutting edge works on adaptive, self-adaptive and multilevel metaheuristics, with application to both combinatorial and continuous optimization.
Differential Evolution From Theory To Practice
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Author : B. Vinoth Kumar
language : en
Publisher: Springer Nature
Release Date : 2022-01-25
Differential Evolution From Theory To Practice written by B. Vinoth Kumar and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-01-25 with Technology & Engineering categories.
This book addresses and disseminates state-of-the-art research and development of differential evolution (DE) and its recent advances, such as the development of adaptive, self-adaptive and hybrid techniques. Differential evolution is a population-based meta-heuristic technique for global optimization capable of handling non-differentiable, non-linear and multi-modal objective functions. Many advances have been made recently in differential evolution, from theory to applications. This book comprises contributions which include theoretical developments in DE, performance comparisons of DE, hybrid DE approaches, parallel and distributed DE for multi-objective optimization, software implementations, and real-world applications. The book is useful for researchers, practitioners, and students in disciplines such as optimization, heuristics, operations research and natural computing.
Evolutionary Algorithms In Intelligent Systems
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Author : Alfredo Milani
language : en
Publisher: MDPI
Release Date : 2020-12-07
Evolutionary Algorithms In Intelligent Systems written by Alfredo Milani and has been published by MDPI this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-07 with Technology & Engineering categories.
Evolutionary algorithms and metaheuristics are widely used to provide efficient and effective approximate solutions to computationally hard optimization problems. With the widespread use of intelligent systems in recent years, evolutionary algorithms have been applied, beyond classical optimization problems, to AI system parameter optimization and the design of artificial neural networks and feature selection in machine learning systems. This volume will present recent results of applications of the most successful metaheuristics, from differential evolution and particle swarm optimization to artificial neural networks, loT allocation, and multi-objective optimization problems. It will also provide a broad view of the role and the potential of evolutionary algorithms as service components in Al systems.
Adaptation And Hybridization In Computational Intelligence
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Author : Iztok Fister
language : en
Publisher: Springer
Release Date : 2015-01-24
Adaptation And Hybridization In Computational Intelligence written by Iztok Fister and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-01-24 with Technology & Engineering categories.
This carefully edited book takes a walk through recent advances in adaptation and hybridization in the Computational Intelligence (CI) domain. It consists of ten chapters that are divided into three parts. The first part illustrates background information and provides some theoretical foundation tackling the CI domain, the second part deals with the adaptation in CI algorithms, while the third part focuses on the hybridization in CI. This book can serve as an ideal reference for researchers and students of computer science, electrical and civil engineering, economy, and natural sciences that are confronted with solving the optimization, modeling and simulation problems. It covers the recent advances in CI that encompass Nature-inspired algorithms, like Artificial Neural networks, Evolutionary Algorithms and Swarm Intelligence –based algorithms.
Swarm Intelligence And Bio Inspired Computation
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Author : Iztok Fister
language : en
Publisher: Elsevier Inc. Chapters
Release Date : 2013-05-16
Swarm Intelligence And Bio Inspired Computation written by Iztok Fister and has been published by Elsevier Inc. Chapters this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-05-16 with Computers categories.
The “firefly algorithm” (FFA) is a modern metaheuristic algorithm, inspired by the behavior of fireflies. This algorithm and its variants have been successfully applied to many continuous optimization problems. This work analyzes the performance of the FFA when solving combinatorial optimization problems. In order to improve the results, the original FFA is extended and improved for self-adaptation of control parameters, and thus more directly balancing between exploration and exploitation in the search process of fireflies. We use a new population model to increase the selection pressure, and the next generation selects only the fittest between a parent and an offspring population. As a result, the proposed memetic self-adaptive FFA (MSA-FFA) is compared with other well-known graph coloring algorithms such as Tabucol, the hybrid evolutionary algorithm, and an evolutionary algorithm with stepwise adaptation of weights. Various experiments have been conducted on a huge set of randomly generated graphs. The results of these experiments show that the results of the MSA-FFA are comparable with other tested algorithms.
Evolutionary Computation Theory And Applications
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Author : Xin Yao
language : en
Publisher: World Scientific
Release Date : 1999-11-22
Evolutionary Computation Theory And Applications written by Xin Yao and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999-11-22 with Computers categories.
Evolutionary computation is the study of computational systems which use ideas and get inspiration from natural evolution and adaptation. This book is devoted to the theory and application of evolutionary computation. It is a self-contained volume which covers both introductory material and selected advanced topics. The book can roughly be divided into two major parts: the introductory one and the one on selected advanced topics. Each part consists of several chapters which present an in-depth discussion of selected topics. A strong connection is established between evolutionary algorithms and traditional search algorithms. This connection enables us to incorporate ideas in more established fields into evolutionary algorithms. The book is aimed at a wide range of readers. It does not require previous exposure to the field since introductory material is included. It will be of interest to anyone who is interested in adaptive optimization and learning. People in computer science, artificial intelligence, operations research, and various engineering fields will find it particularly interesting. Contents:Introduction (X Yao)Evolutionary Computation in Behavior Engineering (M Colombetti & M Dorigo)A General Method for Incremental Self-Improvement and Multi-Agent Learning (J Schmidhuber)Teacher: A Genetics-Based System for Learning and for Generalizing Heuristics (B W Wah & A Ieumwananonthachai)Automatic Discovery of Protein Motifs Using Genetic Programming (J R Koza & D Andre)The Role of Self Organization in Evolutionary Computations (A C Tsoi & J Shaw)Virus-Evolutionary Genetic Algorithm and Its Application to Traveling Salesman Problem (T Fukuda et al.)Hybrid Evolutionary Optimization Algorithm for Constrained Problems (J-H Kim & H Myung)CAM-BRAIN — The Evolutionary Engineering of a Billion Neuron Artificial Brain (H de Garis)An Evolutionary Approach to the N-Player Iterated Prisoner's Dilemma Game (X Yao & Darwen) Readership: Graduate students, practitioners and researchers in engineering and electronics and computer science. keywords:Genetic Algorithms;Evolutionary Computation;Evolutionary Algorithms;Genetic Programming;Evolutionary Robotics;Global Optimization;Evolutionary Games;Global Optimization;Machine Learning;Artificial Intelligence
Introduction To Evolutionary Computing
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Author : A.E. Eiben
language : en
Publisher: Springer
Release Date : 2015-07-01
Introduction To Evolutionary Computing written by A.E. Eiben and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-07-01 with Computers categories.
The overall structure of this new edition is three-tier: Part I presents the basics, Part II is concerned with methodological issues, and Part III discusses advanced topics. In the second edition the authors have reorganized the material to focus on problems, how to represent them, and then how to choose and design algorithms for different representations. They also added a chapter on problems, reflecting the overall book focus on problem-solvers, a chapter on parameter tuning, which they combined with the parameter control and "how-to" chapters into a methodological part, and finally a chapter on evolutionary robotics with an outlook on possible exciting developments in this field. The book is suitable for undergraduate and graduate courses in artificial intelligence and computational intelligence, and for self-study by practitioners and researchers engaged with all aspects of bioinspired design and optimization.
Evolutionary Computation In Combinatorial Optimization
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Author : Günther R. Raidl
language : en
Publisher: Springer
Release Date : 2005-02-26
Evolutionary Computation In Combinatorial Optimization written by Günther R. Raidl and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-02-26 with Computers categories.
This volume contains the proceedings of EvoCOP 2005, the 5th European Conference on Evolutionary Computation in Combinatorial Optimization. It was held in Lausanne, Switzerland, on 30 March–1 April 2005...
Evolutionary Algorithms
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Author : Alain Petrowski
language : en
Publisher: John Wiley & Sons
Release Date : 2017-04-12
Evolutionary Algorithms written by Alain Petrowski and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-04-12 with Computers categories.
Evolutionary algorithms are bio-inspired algorithms based on Darwin’s theory of evolution. They are expected to provide non-optimal but good quality solutions to problems whose resolution is impracticable by exact methods. In six chapters, this book presents the essential knowledge required to efficiently implement evolutionary algorithms. Chapter 1 describes a generic evolutionary algorithm as well as the basic operators that compose it. Chapter 2 is devoted to the solving of continuous optimization problems, without constraint. Three leading approaches are described and compared on a set of test functions. Chapter 3 considers continuous optimization problems with constraints. Various approaches suitable for evolutionary methods are presented. Chapter 4 is related to combinatorial optimization. It provides a catalog of variation operators to deal with order-based problems. Chapter 5 introduces the basic notions required to understand the issue of multi-objective optimization and a variety of approaches for its application. Finally, Chapter 6 describes different approaches of genetic programming able to evolve computer programs in the context of machine learning.