Higher Education Institutions (HEIs) around the world are being pressured to become more entrepreneurial. However, the concept of an entrepreneurial university has remained elusive, including ideas that range from supporting students and staff with new ventures to encouraging partnerships between academics and entrepreneurs. New research is needed on strategies and practices that can be implemented by universities in order to become more innovative and supportive. Strategies for the Creation and Maintenance of Entrepreneurial Universities uses findings from a major EU-funded five country project (THEI2.0) focused on enhancing the implementation and impact of the EU-OECD's HEInnovate tool to offer valuable strategies to help universities become more entrepreneurial, especially in the current COVID-19 and post-COVID-19 environments. This book's core value lies in the fact that it draws on real experiences and practices of those in this field, articulates key takeaway messages, and suggests potential strategies and actions to create impact. Covering topics such as campus incubation, policy strategies, and regional development, this book acts as an essential resource for senior academic leaders, academic managers, entrepreneurship/entrepreneurial educators, incubation center managers, technology transfer managers, researchers, students, and administrators seeking to make their university more entrepreneurial, maintain their entrepreneurial status, critically reflect on their current level of entrepreneurialism, explore new opportunities to enhance their entrepreneurial reputation, or implement strategies to consolidate their entrepreneurial endeavors within the current challenging environment.

Just over thirty years after Holland first presented the outline for Learning Classifier System paradigm, the ability of LCS to solve complex real-world problems is becoming clear. In particular, their capability for rule induction in data mining has sparked renewed interest in LCS. This book brings together work by a number of individuals who are demonstrating their good performance in a variety of domains.

The first contribution is arranged as follows: Firstly, the main forms of LCS are described in some detail. A number of historical uses of LCS in data mining are then reviewed before an overview of the rest of the volume is presented. The rest of this book describes recent research on the use of LCS in the main areas of machine learning data mining: classification, clustering, time-series and numerical prediction, feature selection, ensembles, and knowledge discovery.

This volume includes extended and revised versions of the papers presented at the 9th and 10th International Workshops on Learning Classi?er Systems (IWLCS 2006 and IWLCS 2007). Both workshops were held in association with theGeneticandEvolutionaryComputationConference(GECCO).IWLCS2006 was held on July 8th, 2006, in Seattle, USA, during GECCO 2006.IWLCS 2007 was held on July 8th, 2007, in London, UK, during GECCO 2007. The IWLCS is the annual meeting of researchers wishing to discuss recent developments in learning classi?er systems (LCS). At the last IWLCS, the LCS researchers commemorated the 10th anniversary of the workshop and ackno- edged the contribution of Stewart Wilson to the ?eld. Following his proposal of the XCS classi?er system in 1995, research on LCS was reactivated leading to signi?cant contributions and promising perspectives. The annual IWLCS wo- shops are the proof of this fruitful research. We include an invited paper from Stewart Wilson. We greatly appreciate his contribution to the volume. The contents of this book are as follows. First, Bacardit, Bernado -Mansilla and Butz review LCS research over the past ten years and point out new ch- lenges and open issues in the LCS ?eld. Next, papers investigating knowledge representations are presented. Lanzi et al. analyze the evolution of XCS with symbolic representations using a novel method that identi?es useful substr- tures and tracks the emergence of optimal solutions. Ioannides and Browne investigate the scaling of LCSs using ternary and symbolic representations."

Just over thirty years after Holland first presented the outline for Learning Classifier System paradigm, the ability of LCS to solve complex real-world problems is becoming clear. In particular, their capability for rule induction in data mining has sparked renewed interest in LCS. This book brings together work by a number of individuals who are demonstrating their good performance in a variety of domains.

The first contribution is arranged as follows: Firstly, the main forms of LCS are described in some detail. A number of historical uses of LCS in data mining are then reviewed before an overview of the rest of the volume is presented. The rest of this book describes recent research on the use of LCS in the main areas of machine learning data mining: classification, clustering, time-series and numerical prediction, feature selection, ensembles, and knowledge discovery.

Higher Education Institutions (HEIs) around the world are being pressured to become more entrepreneurial. However, the concept of an entrepreneurial university has remained elusive, including ideas that range from supporting students and staff with new ventures to encouraging partnerships between academics and entrepreneurs. New research is needed on strategies and practices that can be implemented by universities in order to become more innovative and supportive. Strategies for the Creation and Maintenance of Entrepreneurial Universities uses findings from a major EU-funded five country project (THEI2.0) focused on enhancing the implementation and impact of the EU-OECD's HEInnovate tool to offer valuable strategies to help universities become more entrepreneurial, especially in the current COVID-19 and post-COVID-19 environments. This book's core value lies in the fact that it draws on real experiences and practices of those in this field, articulates key takeaway messages, and suggests potential strategies and actions to create impact. Covering topics such as campus incubation, policy strategies, and regional development, this book acts as an essential resource for senior academic leaders, academic managers, entrepreneurship/entrepreneurial educators, incubation center managers, technology transfer managers, researchers, students, and administrators seeking to make their university more entrepreneurial, maintain their entrepreneurial status, critically reflect on their current level of entrepreneurialism, explore new opportunities to enhance their entrepreneurial reputation, or implement strategies to consolidate their entrepreneurial endeavors within the current challenging environment.