Juergen Branke – författare

This book constitutes the refereed proceedings of the 13th International Conference on Parallel Problem Solving from Nature, PPSN 2013, held in Ljubljana, Slovenia, in September 2014. The total of 90 revised full papers were carefully reviewed and selected from 217 submissions. The meeting began with 7 workshops which offered an ideal opportunity to explore specific topics in evolutionary computation, bio-inspired computing and metaheuristics. PPSN XIII also included 9 tutorials. The papers are organized in topical sections on adaption, self-adaption and parameter tuning; classifier system, differential evolution and swarm intelligence; coevolution and artificial immune systems; constraint handling; dynamic and uncertain environments; estimation of distribution algorithms and metamodelling; genetic programming; multi-objective optimisation; parallel algorithms and hardware implementations; real world applications; and theory.

Computing systems including hardware, software, communication, and networks are becoming increasingly large and heterogeneous. In short, they have become - creasingly complex. Such complexity is getting even more critical with the ubiquitous permeation of embedded devices and other pervasive systems. To cope with the growing and ubiquitous complexity, autonomic computing (AC) focuses on self-manageable computing and communication systems that exhibit self-awareness, self-configuration, self-optimization, self-healing, self-protection and other self-* properties to the maximum extent possible without human intervention or guidance. Organic computing (OC) additionally addresses adaptability, robustness, and c- trolled emergence as well as nature-inspired concepts for self-organization. Any autonomic or organic system must be trustworthy to avoid the risk of losing control and retain confidence that the system will not fail. Trust and/or distrust relationships in the Internet and in pervasive infrastructures are key factors to enable dynamic interaction and cooperation of various users, systems, and services. Trusted/ trustworthy computing (TC) aims at making computing and communication systems––as well as services––available, predictable, traceable, controllable, asse- able, sustainable, dependable, persistent, security/privacy protectable, etc. A series of grand challenges exists to achieve practical autonomic or organic s- tems with truly trustworthy services. Started in 2005, ATC conferences have been held at Nagasaki (Japan), Vienna (Austria), Three Gorges (China), Hong Kong (China), Oslo (Norway) and Brisbane (Australia). The 2010 proceedings contain the papers presented at the 7th International Conference on Autonomic and Trusted Computing (ATC 2010), heldin Xi’an, China, October 26–29, 2010.

Computing systems including hardware, software, communication, and networks are becoming increasingly large and heterogeneous. In short, they have become - creasingly complex. Such complexity is getting even more critical with the ubiquitous permeation of embedded devices and other pervasive systems. To cope with the growing and ubiquitous complexity, autonomic computing (AC) focuses on self-manageable computing and communication systems that exhibit self-awareness, self-configuration, self-optimization, self-healing, self-protection and other self-* properties to the maximum extent possible without human intervention or guidance. Organic computing (OC) additionally addresses adaptability, robustness, and c- trolled emergence as well as nature-inspired concepts for self-organization. Any autonomic or organic system must be trustworthy to avoid the risk of losing control and retain confidence that the system will not fail. Trust and/or distrust relationships in the Internet and in pervasive infrastructures are key factors to enable dynamic interaction and cooperation of various users, systems, and services. Trusted/ trustworthy computing (TC) aims at making computing and communication systems––as well as services––available, predictable, traceable, controllable, asse- able, sustainable, dependable, persistent, security/privacy protectable, etc. A series of grand challenges exists to achieve practical autonomic or organic s- tems with truly trustworthy services. Started in 2005, ATC conferences have been held at Nagasaki (Japan), Vienna (Austria), Three Gorges (China), Hong Kong (China), Oslo (Norway) and Brisbane (Australia). The 2010 proceedings contain the papers presented at the 7th International Conference on Autonomic and Trusted Computing (ATC 2010), heldin Xi’an, China, October 26–29, 2010.

ThisLNCSvolumecontainsthepaperspresentedatthe8thSimulatedEvolution and Learning (SEAL 2010) Conference held during December 1-4, 2010 at the Indian Institute of Technology Kanpur in India. SEAL is a prestigious inter- tional conference series in evolutionaryoptimization and machine learning. This biennial event started in Seoul, South Korea in 1996 and was thereafter held in Canberra, Australia in 1998, Nagoya, Japan in 2000, Singapore in 2002, Busan, South Korea in 2004,Hefei, China in 2006and Melbourne, Australia in 2008. SEAL 2010 received 141 paper submissions in total from 30 countries. After a rigorous peer-review process involving 431 reviews in total (averaging a little morethan3reviewsperpaper),60full-lengthand19shortpaperswereaccepted for presentation (both oral and poster) at the conference. The full-length papers alonecorrespondtoa42. 6%acceptancerateandshortpapersaddanother13. 5%. ThepapersincludedinthisLNCSvolumecoverawiderangeoftopicsinsi- latedevolutionandlearning. Theacceptedpapershavebeenclassi?edintothef- lowingmaincategories:(a)theoreticaldevelopments,(b)evolutionaryalgorithms andapplications,(c)learningmethodologies,(d)multi-objectiveevolutionary- gorithms and applications,(e) hybrid algorithms and (f) industrial applications.The conference featured three distinguished keynote speakers. Narendra Karmarkar's talk on "Beyond Convexity: New Perspectives in Computational Optimization" focused on providing new theoretical concepts for non-convex optimization and indicated a rich connection between optimization and ma- ematical physics and also showed a deep signi?cance of advanced geometry to optimization. The advancement of optimization theory for non-convex problems is bene?cial for meta-heuristic optimization algorithms such as evolutionary - gorithms. Manindra Agrawal's talk on "PRIMES is in P" provided a mu- improved version of his celebrated and ground-breaking 2002 work on poly- mial time algorithm for testing prime numbers. The theoretical computation work presented in this keynote lecture should be motivating for the evolutionary optimization and machine learning community at large.

This two-volume set LNCS 15512-15513 constitutes the proceedings of the 13th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2025, held in Canberra, ACT, Australia, in March 2025.The 38 full papers and 2 extended abstracts presented in this book were carefully reviewed and selected from 63 submissions. The papers are divided into the following topical sections:Part I : Algorithm design; Benchmarking; Applications.Part II : Algorithm analysis; Surrogates and machine learning; Multi-criteria decision support.

This two-volume set LNCS 15512-15513 constitutes the proceedings of the 13th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2025, held in Canberra, ACT, Australia, in March 2025.The 38 full papers and 2 extended abstracts presented in this book were carefully reviewed and selected from 63 submissions. The papers are divided into the following topical sections: Part I : Algorithm design; Benchmarking; Applications.Part II : Algorithm analysis; Surrogates and machine learning; Multi-criteria decision support.