Sebanyak 375 item atau buku ditemukan

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 ...

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 ...

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 ...

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 ...

Evolutionary Computation in Combinatorial Optimization

6th European Conference, EvoCOP 2006, Budapest, Hungary, April 10-12, 2006, Proceedings

Constructive greedy heuristics are algorithms that try to iteratively construct
feasible solutions for combinatorial optimization problems from the scratch. For
this they make use of a greedy scoring function, which evaluates the myopic
impact of ...

Experimental Research in Evolutionary Computation

The New Experimentalism

This book introduces the new experimentalism in evolutionary computation, providing tools to understand algorithms and programs and their interaction with optimization problems. It develops and applies statistical techniques to analyze and compare modern search heuristics such as evolutionary algorithms and particle swarm optimization. The book bridges the gap between theory and experiment by providing a self-contained experimental methodology and many examples.

—Ian Hacking In this chapter we discuss the role of experiments in evolutionary
computation. First, problems related to experiments are presented. Objections
stated by theoreticians, for example, “Algorithms are formal objects and should
be ...

Towards a New Evolutionary Computation

Advances on Estimation of Distribution Algorithms

Estimation of Distribution Algorithms (EDAs) are a set of algorithms in the Evolutionary Computation (EC) field characterized by the use of explicit probability distributions in optimization. Contrarily to other EC techniques such as the broadly known Genetic Algorithms (GAs) in EDAs, the crossover and mutation operators are substituted by the sampling of a distribution previously learnt from the selected individuals. EDAs have experienced a high development that has transformed them into an established discipline within the EC field. This book attracts the interest of new researchers in the EC field as well as in other optimization disciplines, and that it becomes a reference for all of us working on this topic. The twelve chapters of this book can be divided into those that endeavor to set a sound theoretical basis for EDAs, those that broaden the methodology of EDAs and finally those that have an applied objective.

Advances on Estimation of Distribution Algorithms Jose A. Lozano. 1 Department
of Information and Communications Gwangju Institute of Science and
Technology (GIST) Oryong-dong, Buk-gu, Gwangju 500-712, Korea ...

Analog Circuits and Systems Optimization based on Evolutionary Computation Techniques

The microelectronics market, with special emphasis to the production of complex mixed-signal systems-on-chip (SoC), is driven by three main dynamics, time-- market, productivity and managing complexity. Pushed by the progress in na- meter technology, the design teams are facing a curve of complexity that grows exponentially, thereby slowing down the productivity design rate. Analog design automation tools are not developing at the same pace of technology, once custom design, characterized by decisions taken at each step of the analog design flow, - lies most of the time on designer knowledge and expertise. Actually, the use of - sign management platforms, like the Cadences Virtuoso platform, with a set of - tegrated CAD tools and database facilities to deal with the design transformations from the system level to the physical implementation, can significantly speed-up the design process and enhance the productivity of analog/mixed-signal integrated circuit (IC) design teams. These design management platforms are a valuable help in analog IC design but they are still far behind the development stage of design automation tools already available for digital design. Therefore, the development of new CAD tools and design methodologies for analog and mixed-signal ICs is ess- tial to increase the designer’s productivity and reduce design productivitygap. The work presented in this book describes a new design automation approach to the problem of sizing analog ICs.

This will be the subject of the next section where an analog design optimization
tool matching the analog IC design problems (for the design of IC circuits) based
on a standard stochastic evolutionary algorithm will be presented in detail. Fig.