Robert Schaefer – författare

What is worse than having a high-stakes conversation? Avoiding one. By our basic nature, many of us have difficulty handling emotionally charged conversations. Our capacity to manage emotions, handle conflict, and communicate authentically is not innate - it must be learned. Meaningful Alignment will provide you with well-defined, practical, and effective tools to manage your emotions and deftly facilitate the emotions of others during a tough conversation. Diminish stress with the skills outlined in this book and experience more satisfaction from your relationships, at home and at work. About the storyCarl is an overworked, stressed-out executive who feels he is losing control in many areas of his life. At work, his productivity deteriorates, and he clashes with a peer. His home situation is not much better: he quarrels with his wife, and his son has stopped communicating with him. Carl feels as though his life is on the verge of imploding as multiple difficulties bring him to the brink of disaster. With the help of a coach and a wise mentor, Carl learns how to build emotional resilience and facilitate the emotions of others - without losing control. Using the Meaningful Alignment program methods, Carl learns to manage his emotions and skillfully navigate high-stakes conversations, with composure and resilience.

Genetic algorithms today constitute a family of e?ective global optimization methods used to solve di?cult real-life problems which arise in science and technology. Despite their computational complexity, they have the ability to explore huge data sets and allow us to study exceptionally problematic cases in which the objective functions are irregular and multimodal, and where information about the extrema location is unobtainable in other ways. Theybelongtotheclassofiterativestochasticoptimizationstrategiesthat, during each step, produce and evaluate the set of admissible points from the search domain, called the random sample or population. As opposed to the Monte Carlo strategies, in which the population is sampled according to the uniform probability distribution over the search domain, genetic algorithms modify the probability distribution at each step. Mechanisms which adopt sampling probability distribution are transposed from biology. They are based mainly on genetic code mutation and crossover, as well as on selection among living individuals. Such mechanisms have been testedbysolvingmultimodalproblemsinnature,whichiscon?rmedinpart- ular by the many species of animals and plants that are well ?tted to di?erent ecological niches. They direct the search process, making it more e?ective than a completely random one (search with a uniform sampling distribution). Moreover,well-tunedgenetic-basedoperationsdonotdecreasetheexploration ability of the whole admissible set, which is vital in the global optimization process. The features described above allow us to regard genetic algorithms as a new class of arti?cial intelligence methods which introduce heuristics, well tested in other ?elds, to the classical scheme of stochastic global search.

Genetic algorithms today constitute a family of e?ective global optimization methods used to solve di?cult real-life problems which arise in science and technology. Despite their computational complexity, they have the ability to explore huge data sets and allow us to study exceptionally problematic cases in which the objective functions are irregular and multimodal, and where information about the extrema location is unobtainable in other ways. Theybelongtotheclassofiterativestochasticoptimizationstrategiesthat, during each step, produce and evaluate the set of admissible points from the search domain, called the random sample or population. As opposed to the Monte Carlo strategies, in which the population is sampled according to the uniform probability distribution over the search domain, genetic algorithms modify the probability distribution at each step. Mechanisms which adopt sampling probability distribution are transposed from biology. They are based mainly on genetic code mutation and crossover, as well as on selection among living individuals. Such mechanisms have been testedbysolvingmultimodalproblemsinnature,whichiscon?rmedinpart- ular by the many species of animals and plants that are well ?tted to di?erent ecological niches. They direct the search process, making it more e?ective than a completely random one (search with a uniform sampling distribution). Moreover,well-tunedgenetic-basedoperationsdonotdecreasetheexploration ability of the whole admissible set, which is vital in the global optimization process. The features described above allow us to regard genetic algorithms as a new class of arti?cial intelligence methods which introduce heuristics, well tested in other ?elds, to the classical scheme of stochastic global search.

Genetic algorithms today constitute a family of e?ective global optimization methods used to solve di?cult real-life problems which arise in science and technology. Despite their computational complexity, they have the ability to explore huge data sets and allow us to study exceptionally problematic cases in which the objective functions are irregular and multimodal, and where information about the extrema location is unobtainable in other ways. Theybelongtotheclassofiterativestochasticoptimizationstrategiesthat, during each step, produce and evaluate the set of admissible points from the search domain, called the random sample or population. As opposed to the Monte Carlo strategies, in which the population is sampled according to the uniform probability distribution over the search domain, genetic algorithms modify the probability distribution at each step. Mechanisms which adopt sampling probability distribution are transposed from biology. They are based mainly on genetic code mutation and crossover, as well as on selection among living individuals. Such mechanisms have been testedbysolvingmultimodalproblemsinnature,whichiscon?rmedinpart- ular by the many species of animals and plants that are well ?tted to di?erent ecological niches. They direct the search process, making it more e?ective than a completely random one (search with a uniform sampling distribution). Moreover,well-tunedgenetic-basedoperationsdonotdecreasetheexploration ability of the whole admissible set, which is vital in the global optimization process. The features described above allow us to regard genetic algorithms as a new class of arti?cial intelligence methods which introduce heuristics, well tested in other ?elds, to the classical scheme of stochastic global search.

We are very pleased to present to you this LNCS volume, the proceedings of the 11th International Conference on Parallel Problem Solving from Nature (PPSN 2010). PPSN is one of the most respected and highly regarded c- ference series in evolutionary computation, and indeed in natural computation aswell.Thisbiennialeventwas?rstheldinDortmundin1990,andtheninBr- sels (1992), Jerusalem (1994), Berlin (1996), Amsterdam (1998), Paris (2000), Granada (2002), Birmingham (2004), Reykjavik (2006) and again in Dortmund in 2008. PPSN 2010 received 232 submissions. After an extensive peer review p- cess involving more than 180 reviewers, the program committee chairs went through all the review reports and ranked the papers according to the revi- ers'comments. Each paper wasevaluated by at least three reviewers.Additional reviewers from the appropriate branches of science were invoked to review into disciplinary papers. The top 128 papers were ?nally selected for inclusion in the proceedings and presentation at the conference.This represents an acceptance rate of 55%, which guarantees that PPSN will continue to be one of the c- ferences of choice for bio-inspired computing and metaheuristics researchers all over the world who value the quality over the size of a conference. The papers included in the proceedingsvolumes covera wide range of topics, fromevolutionarycomputationto swarmintelligence, frombio-inspiredcomp- ing to real-world applications. Machine learning and mathematical games s- portedbyevolutionaryalgorithmsaswellasmemetic,agent-orientedsystemsare also represented. They all are the latest and best in natural computation. The proceedings are composed of two volumes divided into nine thematic sections.

We are very pleased to present to you this LNCS volume, the proceedings of the 11th International Conference on Parallel Problem Solving from Nature (PPSN 2010). PPSN is one of the most respected and highly regarded c- ference series in evolutionary computation, and indeed in natural computation aswell.Thisbiennialeventwas?rstheldinDortmundin1990,andtheninBr- sels (1992), Jerusalem (1994), Berlin (1996), Amsterdam (1998), Paris (2000), Granada (2002), Birmingham (2004), Reykjavik (2006) and again in Dortmund in 2008. PPSN 2010 received 232 submissions. After an extensive peer review p- cess involving more than 180 reviewers, the program committee chairs went through all the review reports and ranked the papers according to the revi- ers'comments. Each paper wasevaluated by at least three reviewers.Additional reviewers from the appropriate branches of science were invoked to review into disciplinary papers. The top 128 papers were ?nally selected for inclusion in the proceedings and presentation at the conference.This represents an acceptance rate of 55%, which guarantees that PPSN will continue to be one of the c- ferences of choice for bio-inspired computing and metaheuristics researchers all over the world who value the quality over the size of a conference. The papers included in the proceedingsvolumes covera wide range of topics, fromevolutionarycomputationto swarmintelligence, frombio-inspiredcomp- ing to real-world applications. Machine learning and mathematical games s- portedbyevolutionaryalgorithmsaswellasmemetic,agent-orientedsystemsare also represented. They all are the latest and best in natural computation. The proceedings are composed of two volumes divided into nine thematic sections.

Der Klinikmarkt ist hart umkämpft -nur wer gesehen wird von seinen Patienten und Kunden, bleibt oben. Wie Sie die Aufmerksamkeit Ihrer Zielgruppen gewinnen, zeigt das vorliegende Buch anhand zahlreicher praktischer Beispiele auf: Mit welchen Stories kommen Sie in die Medien? Wie werden Ihre Veranstaltungen zum Besuchermagnet? Wie sehen gute Internet-Auftritte aus? Der Autor Robert Schäfer ist Experte für Gesundheitskommunikation. Seit vielen Jahren berät er Klinikketten, Chefärzte und viele weitere Akteure der Gesundheitswirtschaft bei der treffsicheren Platzierung von Marketing- und PR-Kampagnen. Im Fokus dieses Ratgebers stehen die Instrumente Pressearbeit, Veranstaltungsmanagement und effiziente Online-Auftritte. Klinikverantwortliche, die ihre Einrichtung erfolgreich positionieren wollen, finden konkrete How-to-Tipps für die Patientengewinnung. Dozenten und Studierende des Faches Gesundheitsmanagement erhalten einen Überblick über die wichtigsen Erfolgsfaktoren und Umsetzungsstrategien des Gesundheitsmarketing und der -kommunikation.