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This textbook is a second edition of Evolutionary Algorithms for
Solving Multi-Objective Problems, significantly expanded and
adapted for the classroom. The various features of multi-objective
evolutionary algorithms are presented here in an innovative and
student-friendly fashion, incorporating state-of-the-art research.
The book disseminates the application of evolutionary algorithm
techniques to a variety of practical problems. It contains
exhaustive appendices, index and bibliography and links to a
complete set of teaching tutorials, exercises and solutions.
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Advances in Swarm Intelligence - 5th International Conference, ICSI 2014, Hefei, China, October 17-20, 2014, Proceedings, Part I (Paperback, 2014 ed.)
Ying Tan, Yuhui Shi, Carlos Coello Coello
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R3,161
Discovery Miles 31 610
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Ships in 10 - 15 working days
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This book and its companion volume, LNCS vol. 8794 and 8795
constitute the proceedings of the 5th International Conference on
Swarm Intelligence, ICSI 2014, held in Hefei, China in October
2014. The 107 revised full papers presented were carefully reviewed
and selected from 198 submissions. The papers are organized in 18
cohesive sections, 3 special sessions and one competitive session
covering all major topics of swarm intelligence research and
development such as novel swarm-based search methods; novel
optimization algorithm; particle swarm optimization; ant colony
optimization for travelling salesman problem; artificial bee colony
algorithms; artificial immune system; evolutionary algorithms;
neural networks and fuzzy methods; hybrid methods; multi-objective
optimization; multi-agent systems; evolutionary clustering
algorithms; classification methods; GPU-based methods; scheduling
and path planning; wireless sensor networks; power system
optimization; swarm intelligence in image and video processing;
applications of swarm intelligence to management problems; swarm
intelligence for real-world application.
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Parallel Problem Solving from Nature - PPSN XII - 12th International Conference, Taormina, Italy, September 1-5, 2012, Proceedings, Part I (Paperback, 2012 ed.)
Carlos Coello Coello, Vincenzo Cutello, Kalyanmoy Deb, Stephanie Forrest, Giuseppe Nicosia, …
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R1,643
Discovery Miles 16 430
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Ships in 10 - 15 working days
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The two volume set LNCS 7491 and 7492 constitutes the refereed
proceedings of the 12th International Conference on Parallel
Problem Solving from Nature, PPSN 2012, held in Taormina, Sicily,
Italy, in September 2012. The total of 105 revised full papers were
carefully reviewed and selected from 226 submissions. The meeting
began with 5 workshops which offered an ideal opportunity to
explore specific topics in evolutionary computation, bio-inspired
computing and metaheuristics. PPSN 2012 also included 8 tutorials.
The papers are organized in topical sections on evolutionary
computation; machine learning, classifier systems, image
processing; experimental analysis, encoding, EDA, GP;
multiobjective optimization; swarm intelligence, collective
behavior, coevolution and robotics; memetic algorithms, hybridized
techniques, meta and hyperheuristics; and applications.
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Parallel Problem Solving from Nature - PPSN XII - 12th International Conference, Taormina, Italy, September 1-5, 2012, Proceedings, Part II (Paperback, 2012 ed.)
Carlos Coello Coello, Vincenzo Cutello, Kalyanmoy Deb, Stephanie Forrest, Giuseppe Nicosia, …
|
R1,639
Discovery Miles 16 390
|
Ships in 10 - 15 working days
|
The two volume set LNCS 7491 and 7492 constitutes the refereed
proceedings of the 12th International Conference on Parallel
Problem Solving from Nature, PPSN 2012, held in Taormina, Sicily,
Italy, in September 2012. The total of 105 revised full papers were
carefully reviewed and selected from 226 submissions. The meeting
began with 6 workshops which offered an ideal opportunity to
explore specific topics in evolutionary computation, bio-inspired
computing and metaheuristics. PPSN 2012 also included 8 tutorials.
The papers are organized in topical sections on evolutionary
computation; machine learning, classifier systems, image
processing; experimental analysis, encoding, EDA, GP;
multiobjective optimization; swarm intelligence, collective
behavior, coevolution and robotics; memetic algorithms, hybridized
techniques, meta and hyperheuristics; and applications.
The purpose of this book is to collect contributions that deal with
the use of nature inspired metaheuristics for solving
multi-objective combinatorial optimization problems. Such a
collection intends to provide an overview of the state-of-the-art
developments in this field, with the aim of motivating more
researchers in operations research, engineering, and computer
science, to do research in this area. As such, this book is
expected to become a valuable reference for those wishing to do
research on the use of nature inspired metaheuristics for solving
multi-objective combinatorial optimization problems.
Multi-objective optimization deals with the simultaneous
optimization of two or more objectives which are normally in
con?ict with each other. Since mul- objective optimization problems
are relatively common in real-world appli- tions, this area has
become a very popular research topic since the 1970s. However, the
use of bio-inspired metaheuristics for solving multi-objective op-
mization problems started in the mid-1980s and became popular until
the mid- 1990s. Nevertheless, the e?ectiveness of multi-objective
evolutionary algorithms has made them very popular in a variety of
domains. Swarm intelligence refers to certain population-based
metaheuristics that are inspired on the behavior of groups of
entities (i.e., living beings) interacting
locallywitheachotherandwiththeirenvironment.Suchinteractionsproducean
emergentbehaviorthatismodelledinacomputerinordertosolveproblems.The
two most popular metaheuristics within swarm intelligence are
particle swarm optimization (which simulates a ?ock of birds
seeking food) and ant colony optimization (which simulates the
behavior of colonies of real ants that leave their nest looking for
food). These two metaheuristics havebecome verypopular
inthelastfewyears,
andhavebeenwidelyusedinavarietyofoptimizationtasks, including some
related to data mining and knowledge discovery in databases.
However, such work has been mainly focused on single-objective
optimization models. The use of multi-objective extensions of swarm
intelligence techniques in data mining has been relatively scarce,
in spite of their great potential, which constituted the main
motivation to produce this book
The purpose of this book is to collect contributions that deal with
the use of nature inspired metaheuristics for solving
multi-objective combinatorial optimization problems. Such a
collection intends to provide an overview of the state-of-the-art
developments in this field, with the aim of motivating more
researchers in operations research, engineering, and computer
science, to do research in this area. As such, this book is
expected to become a valuable reference for those wishing to do
research on the use of nature inspired metaheuristics for solving
multi-objective combinatorial optimization problems.
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Evolutionary Multi-Criterion Optimization - Third International Conference, EMO 2005, Guanajuato, Mexico, March 9-11, 2005, Proceedings (Paperback, 2005 ed.)
Carlos Coello Coello, Arturo Hernandez Aguirre, Eckart Zitzler
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R3,155
Discovery Miles 31 550
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Ships in 10 - 15 working days
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Multicriterion optimization refers to problems with two or more
objectives (n- mally in con?ict with each other) which must be
simultaneously satis?ed. Multicriterion optimization problems have
not one but a set of solutions (which represent trade-o's among the
objectives), which are called Pareto optimal - lutions. Thus, the
main goal in multicriterion optimization is to ?nd or to -
proximate the set of Pareto optimal solutions. Evolutionary
algorithms have been used for solving multicriterion optimization
problems for over two decades, gaining an increasing popularity
over the last 10 years. The 3rd International Conference on
Evolutionary Multi-criterion Optimi-
tion(EMO2005)washeldduringMarch9 11,2005, inGuanajuato, M
exico.This
wasthethirdinternationalconferencededicatedentirelytothisimportanttopic,
followingthesuccessfulEMO2001andEMO2003conferences, whichwereheldin
Z] urich, SwitzerlandinMarch2001, andinFaro, PortugalinApril2003,
respectively. The EMO 2005 scienti?c program included two keynote
addresses, one given by Peter Fleming on an engineering design
perspective of many-objective op- mization, and the other given by
Milan Zeleny on the evolution of optimality. In addition, three
tutorials were presented, one on metaheuristics for multiobj-
tivecombinatorialoptimizationbyXavierGandibleux,
anotheronmultiobjective evolutionary algorithms by Gary B. Lamont,
and a third one on performance assessment of multiobjective
evolutionary algorithms by Joshua D. Knowle
This book constitutes the refereed proceedings of the Second Mexican International Conference on Artificial Intelligence, MICAI 2002, held in Mérida, Yucatán, Mexico in April 2002.The 56 revised full papers presented were carefully reviewed and selected from more than 85 submissions from 17 countries. The papers are organized in topical sections on robotics and computer vision, heuristic search and optimization, speech recognition and natural language processing, logic, neural networks, machine learning, multi-agent systems, uncertainty management, and AI tools and applications.
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Evolutionary Multi-Criterion Optimization - First International Conference, EMO 2001, Zurich, Switzerland, March 7-9, 2001 Proceedings (Paperback, 2001 ed.)
Eckart Zitzler, Kalyanmoy Deb, Lothar Thiele, Carlos Coello Coello, David Corne
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R3,334
Discovery Miles 33 340
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Ships in 10 - 15 working days
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This book constitutes the refereed proceedings of the First International Conference on Multi-Criterion Optimization, EMO 2001, held in Zurich, Switzerland in March 2001.The 45 revised full papers presented were carefully reviewed and selected from a total of 87 submissions. Also included are two tutorial surveys and two invited papers. The book is organized in topical sections on algorithm improvements, performance assessment and comparison, constraint handling and problem decomposition, uncertainty and noise, hybrid and alternative methods, scheduling, and applications of multi-objective optimization in a variety of fields.
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Evolutionary Multi-Criterion Optimization - 8th International Conference, EMO 2015, Guimaraes, Portugal, March 29 --April 1, 2015. Proceedings, Part I (Paperback, 2015 ed.)
Antonio Gaspar-Cunha, Carlos Henggeler Antunes, Carlos Coello Coello
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R1,613
Discovery Miles 16 130
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Ships in 10 - 15 working days
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This book constitutes the refereed proceedings of the 8th
International Conference on Evolutionary Multi-Criterion
Optimization, EMO 2015 held in Guimaraes, Portugal in March/April
2015. The 68 revised full papers presented together with 4 plenary
talks were carefully reviewed and selected from 90 submissions. The
EMO 2015 aims to continue these type of developments, being the
papers presented focused in: theoretical aspects, algorithms
development, many-objectives optimization, robustness and
optimization under uncertainty, performance indicators, multiple
criteria decision making and real-world applications.
|
Evolutionary Multi-Criterion Optimization - 8th International Conference, EMO 2015, Guimaraes, Portugal, March 29 --April 1, 2015. Proceedings, Part II (Paperback, 2015 ed.)
Antonio Gaspar-Cunha, Carlos Henggeler Antunes, Carlos Coello Coello
|
R3,396
Discovery Miles 33 960
|
Ships in 10 - 15 working days
|
This book constitutes the refereed proceedings of the 8th
International Conference on Evolutionary Multi-Criterion
Optimization, EMO 2015 held in Guimaraes, Portugal in March/April
2015. The 68 revised full papers presented together with 4 plenary
talks were carefully reviewed and selected from 90 submissions. The
EMO 2015 aims to continue these type of developments, being the
papers presented focused in: theoretical aspects, algorithms
development, many-objectives optimization, robustness and
optimization under uncertainty, performance indicators, multiple
criteria decision making and real-world applications.
This textbook is a second edition of Evolutionary Algorithms for
Solving Multi-Objective Problems, significantly expanded and
adapted for the classroom. The various features of multi-objective
evolutionary algorithms are presented here in an innovative and
student-friendly fashion, incorporating state-of-the-art research.
The book disseminates the application of evolutionary algorithm
techniques to a variety of practical problems. It contains
exhaustive appendices, index and bibliography and links to a
complete set of teaching tutorials, exercises and solutions.
Multi-objective optimization deals with the simultaneous
optimization of two or more objectives which are normally in
con?ict with each other. Since mul- objective optimization problems
are relatively common in real-world appli- tions, this area has
become a very popular research topic since the 1970s. However, the
use of bio-inspired metaheuristics for solving multi-objective op-
mization problems started in the mid-1980s and became popular until
the mid- 1990s. Nevertheless, the e?ectiveness of multi-objective
evolutionary algorithms has made them very popular in a variety of
domains. Swarm intelligence refers to certain population-based
metaheuristics that are inspired on the behavior of groups of
entities (i.e., living beings) interacting
locallywitheachotherandwiththeirenvironment.Suchinteractionsproducean
emergentbehaviorthatismodelledinacomputerinordertosolveproblems.The
two most popular metaheuristics within swarm intelligence are
particle swarm optimization (which simulates a ?ock of birds
seeking food) and ant colony optimization (which simulates the
behavior of colonies of real ants that leave their nest looking for
food). These two metaheuristics havebecome verypopular
inthelastfewyears,
andhavebeenwidelyusedinavarietyofoptimizationtasks, including some
related to data mining and knowledge discovery in databases.
However, such work has been mainly focused on single-objective
optimization models. The use of multi-objective extensions of swarm
intelligence techniques in data mining has been relatively scarce,
in spite of their great potential, which constituted the main
motivation to produce this book
|
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