Mario Giacobini (ed.); Anthony Brabazon (ed.); Stefano Cagnoni (ed.); Aniko Ekart (ed.); Anna I. Esparcia-Alcázar (ed.); Fa Springer (2009) Pehmeäkantinen kirja
Michael O'Neill (ed.); Leonardo Vanneschi (ed.); Steven Gustafson (ed.); Anna Isabel Esparcia Alcázar (ed.); Ivano De Falco Springer (2008) Pehmeäkantinen kirja
Mario Giacobini (ed.); Anthony Brabazon (ed.); Stefano Cagnoni (ed.); Gianni A. Di Caro (ed.); Rolf Drechsler (ed.); Ekart Springer (2008) Pehmeäkantinen kirja
Marc Ebner (ed.); Michael O'Neill (ed.); Anikó Ekárt (ed.); Leonardo Vanneschi (ed.); Anna Isabel Esparcia-Alcázar (ed.) Springer (2007) Pehmeäkantinen kirja
Cecilia Di Chio (ed.); Stefano Cagnoni (ed.); Carlos Cotta (ed.); Marc Ebner (ed.); Aniko Ekart (ed.); An Esparcia-Alcázar Springer (2010) Pehmeäkantinen kirja
Cecilia Di Chio (ed.); Stefano Cagnoni (ed.); Carlos Cotta (ed.); Marc Ebner (ed.); Aniko Ekart (ed.); An Esparcia-Alcázar Springer (2011) Pehmeäkantinen kirja
In its lucky 12+1 edition, during April 7-9, 2010, the European Conference on Genetic Programming (EuroGP) travelled to its most easterly location so far, theEuropeanCityof Culture2010,Istanbul,Turkey.EuroGPisthe onlyconf- enceworldwideexclusivelydevotedtogeneticprogrammingandtheevolutionary generation of computer programs. For over a decade, genetic programming (GP) has been considered the new form of evolutionary computation. With nearly 7,000 articles in the online GP bibliography maintained by William B. Langdon, we can say that it is now a mature ?eld. EuroGP has contributed to the success of the ?eld substantially, by being a unique forum for expressing new ideas, meeting, and starting up collaborations. The wide rangeoftopics in this volume re?ectthecurrentstateof researchin the ?eld, including representations, theory, operators and analysis, novel m- els, performance enhancements, extensions of genetic programming,and various applications. The volume contains contributions in the following areas: - Understanding GP behavior andGP analysis include articles on cro- over operators and a new way of analyzing results. -GPperformance presents work on performance enhancements through phenotypic diversity, simpli?cation, ?tness and parallelism. - Novel models and their application present innovative approaches with arti?cial biochemical networks, genetic regulatory networks and geometric di?erential evolution. - Grammatical evolution introduces advances in crossover, mutation and phenotype-genotype maps in this relatively new area. - Machine learning and data mining include articles that present data miningormachinelearningsolutionsusingGPandalsocombinedatamining and machine learning with GP. - Applications rangefromsolvingdi?erentialequations,routingproblems to ?le type detection, object-oriented testing, agents. This year we received 48 submissions, of which 47 were sent to the reviewers.
