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Evolutionary Computation for Dynamic Optimization Problems

This book provides a compilation on the state-of-the-art and recent advances of evolutionary computation for dynamic optimization problems. The motivation for this book arises from the fact that many real-world optimization problems and engineering systems are subject to dynamic environments, where changes occur over time. Key issues for addressing dynamic optimization problems in evolutionary computation, including fundamentals, algorithm design, theoretical analysis, and real-world applications, are presented. "Evolutionary Computation for Dynamic Optimization Problems" is a valuable reference to scientists, researchers, professionals and students in the field of engineering and science, particularly in the areas of computational intelligence, nature- and bio-inspired computing, and evolutionary computation.

We would like to thank Dr. Janusz Kacprzyk for inviting us to edit this book in the
Springer book series “Studies in Computational Intelligence”. We acknowledge
the contributors for their fine work and cooperation during the book preparation ...

Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics

8th European Conference, EvoBIO 2010, Istanbul, Turkey, April 7-9, 2010, Proceedings

The ?eld of bioinformatics has two main objectives: the creation and main- nance of biological databases, and the discovery of knowledge from life sciences datainordertounravelthemysteriesofbiologicalfunction,leadingtonewdrugs andtherapiesforhumandisease. Life sciencesdatacomeinthe formofbiological sequences, structures, pathways, or literature. One major aspect of discovering biological knowledge is to search, predict, or model speci?c information in a given dataset in order to generate new interesting knowledge. Computer science methods such as evolutionary computation, machine learning, and data mining all have a great deal to o?er the ?eld of bioinformatics. The goal of the 8th - ropean Conference on Evolutionary Computation, Machine Learning, and Data Mining in Bioinformatics (EvoBIO 2010) was to bring together experts in these ?elds in order to discuss new and novel methods for tackling complex biological problems. The 8th EvoBIO conference was held in Istanbul, Turkey during April 7–9, 2010attheIstanbulTechnicalUniversity. EvoBIO2010washeldjointlywiththe 13th European Conference on Genetic Programming (EuroGP 2010), the 10th European Conference on Evolutionary Computation in Combinatorial Opti- sation (EvoCOP 2010), and the conference on the applications of evolutionary computation,EvoApplications. Collectively,the conferences areorganizedunder the name Evo* (www. evostar. org). EvoBIO, held annually as a workshop since 2003, became a conference in 2007 and it is now the premiere European event for those interested in the interface between evolutionary computation, machine learning, data mining, bioinformatics, and computational biology.

8th European Conference, EvoBIO 2010, Istanbul, Turkey, April 7-9, 2010,
Proceedings Clara Pizzuti, Marylyn D. Ritchie, Mario Giacobini. Finding Gapped
Motifs by a Novel Evolutionary Algorithm Chengwei Lei and Jianhua Ruan
Department ...

EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation V

This volume encloses research articles that were presented at the EVOLVE 2014 International Conference in Beijing, China, July 1–4, 2014. The book gathers contributions that emerged from the conference tracks, ranging from probability to set oriented numerics and evolutionary computation; all complemented by the bridging purpose of the conference, e.g. Complex Networks and Landscape Analysis, or by the more application oriented perspective. The novelty of the volume, when considering the EVOLVE series, comes from targeting also the practitioner’s view. This is supported by the Machine Learning Applied to Networks and Practical Aspects of Evolutionary Algorithms tracks, providing surveys on new application areas, as in the networking area and useful insights in the development of evolutionary techniques, from a practitioner’s perspective. Complementary to these directions, the conference tracks supporting the volume, follow on the individual advancements of the subareas constituting the scope of the conference, through the Computational Game Theory, Local Search and Optimization, Genetic Programming, Evolutionary Multi-objective optimization tracks.

This paper presents a study on the application of evolutionary computation and
artificial intelligence techniques to forecast inflows into the Vanderkloof reservoir,
South Africa for the purpose of planning and management of available water ...

Recent Advances in Evolutionary Computation for Combinatorial Optimization

This cutting-edge volume presents recent advances in the area of metaheuristic combinatorial optimisation, with a special focus on evolutionary computation methods. Moreover, it addresses local search methods and hybrid approaches.

9 An Iterative Heuristic Algorithm for Tree Decomposition Nysret Musliu Institute
for Information Systems, Vienna University ... Finding the optimal tree
decompositions is an NP-hard problem and different algorithms have been
proposed in the ...

Advances in Artificial Life and Evolutionary Computation

9th Italian Workshop, WIVACE 2014, Vietri sul Mare, Italy, May 14-15, Revised Selected Papers

This book constitutes the revised selected papers of the 9th Italian Workshop on Advances in Artificial Life and Evolutionary Computation held in Vietri sul Mare, Italy, in May 2014, in conjunction with the 24th Italian Workshop on Neural Networks, WIRN 2014. The 16 papers presented have been thoroughly reviewed and selected from 40 submissions. They cover the following topics: artificial neural networks; fuzzy inference systems; rough set; approximate reasoning; and optimization methods such as evolutionary computation, swarm intelligence, particle swarm optimization.

Examples of the Usage of Infinities and Infinitesimals in Numerical Computations
Yaroslav D. Sergeyev1,2,3(B) 1 ... 2 N.I. Lobatchevsky State University, Nizhni
Novgorod, Russia 3 Institute of High Performance Computing and Networking, ...

Applications of Evolutionary Computation

17th European Conference, EvoApplications 2014, Granada, Spain, April 23-25, 2014, Revised Selected Papers

This book constitutes the thoroughly refereed post-conference proceedings of the International Conference on the Applications of Evolutionary Computation, EvoApplications 2014, held in Granada, Spain, in April 2014, colocated with the Evo* 2014 events EuroGP, EvoCOP, and EvoMUSART. The 79 revised full papers presented were carefully reviewed and selected from 128 submissions. EvoApplications 2014 consisted of the following 13 tracks: EvoCOMNET (nature-inspired techniques for telecommunication networks and other parallel and distributed systems), EvoCOMPLEX (evolutionary algorithms and complex systems), EvoENERGY (evolutionary computation in energy applications), EvoFIN (evolutionary and natural computation in finance and economics), EvoGAMES (bio-inspired algorithms in games), EvoIASP (evolutionary computation in image analysis, signal processing, and pattern recognition), EvoINDUSTRY (nature-inspired techniques in industrial settings), EvoNUM (bio-inspired algorithms for continuous parameter optimization), EvoPAR (parallel implementation of evolutionary algorithms), EvoRISK (computational intelligence for risk management, security and defence applications), EvoROBOT (evolutionary computation in robotics), EvoSTOC (evolutionary algorithms in stochastic and dynamic environments), and EvoBio (EC and related techniques in bioinformatics and computational biology).

... 23-25, 2014, Revised Selected Papers Anna I. Esparcia-Alcázar, Antonio M.
Mora. Volume. Editors. Preface Evolutionary computation (EC) techniques are
efficient, nature-inspired planning and. Anna I. Esparcia-Alcázar S2 Grupo, Spain
 ...

Massively Parallel Evolutionary Computation on GPGPUs

Evolutionary algorithms (EAs) are metaheuristics that learn from natural collective behavior and are applied to solve optimization problems in domains such as scheduling, engineering, bioinformatics, and finance. Such applications demand acceptable solutions with high-speed execution using finite computational resources. Therefore, there have been many attempts to develop platforms for running parallel EAs using multicore machines, massively parallel cluster machines, or grid computing environments. Recent advances in general-purpose computing on graphics processing units (GPGPU) have opened up this possibility for parallel EAs, and this is the first book dedicated to this exciting development. The three chapters of Part I are tutorials, representing a comprehensive introduction to the approach, explaining the characteristics of the hardware used, and presenting a representative project to develop a platform for automatic parallelization of evolutionary computing (EC) on GPGPUs. The 10 chapters in Part II focus on how to consider key EC approaches in the light of this advanced computational technique, in particular addressing generic local search, tabu search, genetic algorithms, differential evolution, swarm optimization, ant colony optimization, systolic genetic search, genetic programming, and multiobjective optimization. The 6 chapters in Part III present successful results from real-world problems in data mining, bioinformatics, drug discovery, crystallography, artificial chemistries, and sudoku. Although the parallelism of EAs is suited to the single-instruction multiple-data (SIMD)-based GPU, there are many issues to be resolved in design and implementation, and a key feature of the contributions is the practical engineering advice offered. This book will be of value to researchers, practitioners, and graduate students in the areas of evolutionary computation and scientific computing.

Evolutionary algorithms (EAs) are generic heuristics that learn from natural
collective behavior and can be applied to solve very difficult optimization
problems. Applications include engineering problems, scheduling problems,
routing problems ...

Recent Advances in Swarm Intelligence and Evolutionary Computation

This timely review volume summarizes the state-of-the-art developments in nature-inspired algorithms and applications with the emphasis on swarm intelligence and bio-inspired computation. Topics include the analysis and overview of swarm intelligence and evolutionary computation, hybrid metaheuristic algorithms, bat algorithm, discrete cuckoo search, firefly algorithm, particle swarm optimization, and harmony search as well as convergent hybridization. Application case studies have focused on the dehydration of fruits and vegetables by the firefly algorithm and goal programming, feature selection by the binary flower pollination algorithm, job shop scheduling, single row facility layout optimization, training of feed-forward neural networks, damage and stiffness identification, synthesis of cross-ambiguity functions by the bat algorithm, web document clustering, truss analysis, water distribution networks, sustainable building designs and others. As a timely review, this book can serve as an ideal reference for graduates, lecturers, engineers and researchers in computer science, evolutionary computing, artificial intelligence, machine learning, computational intelligence, data mining, engineering optimization and designs.

Xin-She Yang. 1. Applegate, D., Cook, W.: A computational study of the jobshop
scheduling problem. ORSA J. comput. 3(2), 149–156 (1991) [CrossRef][MATH] 2.
Manne, A.S.: On the jobshop scheduling problem. Oper. Res. 8(2), 219–223 ...

Evolutionary Computation in Combinatorial Optimization

8th European Conference, EvoCOP 2008, Naples, Italy, March 26-28, 2008, Proceedings

This book constitutes the refereed proceedings of the 8th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2008, held in Naples, Italy, in March 2008. The 24 revised full papers presented were carefully reviewed and selected from 69 submissions. The papers present the latest research and discuss current developments and applications in metaheuristics - a paradigm to effectively solve difficult combinatorial optimization problems appearing in various industrial, economical, and scientific domains. Prominent examples of metaheuristics are evolutionary algorithms, simulated annealing, tabu search, scatter search, memetic algorithms, variable neighborhood search, iterated local search, greedy randomized adaptive search procedures, estimation of distribution algorithms and ant colony optimization.

In our algorithm, those solutions generated by path relinking operations are
improved by a local search whose neighborhood consists of slight modifications
of the representative neighborhoods called 2-opt∗, cross exchange and Or-opt.

Intelligent Financial Portfolio Composition based on Evolutionary Computation Strategies

The management of financial portfolios or funds constitutes a widely known problematic in financial markets which normally requires a rigorous analysis in order to select the most profitable assets. This subject is becoming popular among computer scientists which try to adapt known Intelligent Computation techniques to the market’s domain. This book proposes a potential system based on Genetic Algorithms, which aims to manage a financial portfolio by using technical analysis indicators. The results are promising since the approach clearly outperforms the remaining approaches during the recent market crash.

Abstract This chapter presents a brief description on the problematic addressed
by this book, namely the management of financial portfolios using intelligent
computation techniques. Additionally, the main goals for the work presented in
this ...