Erick Cantú-Paz

Francisco Fernández de Vega

Offering a global snapshot of parallel and distributed computational intelligence today, this volume covers ongoing issues as well as recent exploratory work. Topics discussed include GPUs, Clusters, Grids, volunteer computing, p2p networks and more.

Parallel and Distributed Computational Intelligence

Martin Pelikan

Kumara Sastry

I’m not usually a fan of edited volumes. Too often they are an incoherent hodgepodge of remnants, renegades, or rejects foisted upon an unsuspecting reading public under a misleading or fraudulent title. The volume Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications is a worthy addition to your library because it succeeds on exactly those dimensions where so many edited volumes fail. For example, take the title, Scalable Optimization via Probabilistic M- eling: From Algorithms to Applications. You need not worry that you’re going to pick up this book and ?nd stray articles about anything else. This book focuseslikealaserbeamononeofthehottesttopicsinevolutionary compu- tion over the last decade or so: estimation of distribution algorithms (EDAs). EDAs borrow evolutionary computation’s population orientation and sel- tionism and throw out the genetics to give us a hybrid of substantial power, elegance, and extensibility. The article sequencing in most edited volumes is hard to understand, but from the get go the editors of this volume have assembled a set of articles sequenced in a logical fashion. The book moves from design to e?ciency enhancement and then concludes with relevant applications. The emphasis on e?ciency enhancement is particularly important, because the data-mining perspectiveimplicitinEDAsopensuptheworldofoptimizationtonewme- ods of data-guided adaptation that can further speed solutions through the construction and utilization of e?ective surrogates, hybrids, and parallel and temporal decompositions.

From Algorithms to Applications

Scalable Optimization via Probabilistic Modeling

Graham Kendall

Kalyanmoy Deb

James A. Foster

Lawrence David Davis

Rajkumar Roy

Una-May O'Reilly

Hans-Georg Beyer

Russel Standish

Stewart Wilson

Joachim Wegener

Dipankar Dasgupta

Mitchell A. Potter

Alan C. Schultz

The set LNCS 2723 and LNCS 2724 constitutes the refereed proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2003, held in Chicago, IL, USA in July 2003. The 193 revised full papers and 93 poster papers presented were carefully reviewed and selected from a total of 417 submissions. The papers are organized in topical sections on a-life adaptive behavior, agents, and ant colony optimization; artificial immune systems; coevolution; DNA, molecular, and quantum computing; evolvable hardware; evolutionary robotics; evolution strategies and evolutionary programming; evolutionary sheduling routing; genetic algorithms; genetic programming; learning classifier systems; real-world applications; and search based software engineering.

Genetic and Evolutionary Computation Conference Chicago, IL, USA, July 12–16, 2003 Proceedings

Genetic and Evolutionary Computation — GECCO 2003

Erick Cantú-Paz

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