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They are also encouraged to use the existing database for statistical analyses or for designing a portfolio of algorithms. When data for all algorithms of the portfolio are available in the database, the performance of the portfolio can be provided by the postprocessing without conducting further experiments. An overall analysis and comparison will be accomplished by the organizers and presented during the workshop together with the single presentations of each participant. Our presentation will in particular address the question, whether a significant over-adaptation of algorithms to the benchmark function set has been taken place during the last years and discuss the perspective of how benchmarks should co- evolve.

Her main research interest is stochastic continuous optimization including theoretical aspects and algorithm designs. She has been organizing the biannual Dagstuhl seminar "Theory of Evolutionary Algorithms" in and Alexandre Chotard He graduated in mathematics before obtaining a Master degree in Informatics in from university Paris-Sud. His subject is the enhancement and analysis of evolution strategies.

She received the doctoral degree in physics from the Ruhr-University Bochum in Her research interests are machine learning and stochastic continuous optimization, in particular reinforcement learning and natural gradient descent. She published several articles at top conferences and journals in this area.

Educated in medicine and mathematics, he received a Ph. His main research interests are learning and adaptation in evolutionary computation and the development of algorithms applicable in practice. His best-known contribution to the field of evolutionary computation is the so-called Covariance Matrix Adaptation CMA. He is currently pursuing a PhD in Statistics with work based on his Bachelor Thesis on the design and analysis of benchmark experiments. Part of this work has been presented at conferences and is currently under review for publication.

Using statistical and machine learning methods on large benchmark databases to gain insight into the structure of the algorithm choice problem is one of his current research interests. From to he also worked as statistician, analyst and lecturer for StatSoft, Czech Republic. Being on the boundary of optimization, statistics and machine learning, his research interests are aimed at improving the characteristics of evolutionary algorithms with techniques of statistical machine learning.

He also serves as a reviewer for several journals and conferences in the evolutionary-computation field. His research interests focus on the field of evolutionary algorithms for real-valued problems, namely on multimodal and multiobjective niching and the experimental methodology for non-deterministic optimization algorithms.

He is currently working on the adaptability and applicability of computational intelligence techniques for various engineering domains and computer games, pushing forward modern approaches of experimental analysis as the Exploratory Landscape Analysis ELA and innovative uses of surrogate models. Since Learning Classifier Systems LCSs were introduced by Holland [1] as a way of applying evolutionary computation to machine learning problems, the LCS paradigm has broadened greatly into a framework encompassing many representations, rule discovery mechanisms, and credit assignment schemes.

Current LCS applications range from data mining to automated innovation and on-line control. Classifier systems are a very active area of research, with newer approaches, in particular Wilson's accuracy-based XCS [2], receiving a great deal of attention. LCS are also benefiting from advances in reinforcement learning and other machine learning techniques. Topics of interests include but are not limited to:.

Interest of the workshop to the Genetic and Evolutionary Computation community LCSs have been an integral part of the evolutionary computation field almost since its beginnings, so this workshop is very interesting for the GEC community for itself, but also because it shares many common research topics with the broader GEC field such as linkage learning, niching techniques, variable-length representations, facet-wise models, etc.

Therefore it can attract a broader audience besides the own LCS practitioners. Post-proceedings of the papers accepted for the workshop are published - after an additional selection - as a special issue of the Springer journal Evolutionary Intelligence, which is an extra element of interest for participating in the workshop. In he received the Ph. His research interests include machine learning, evolutionary computation, and computational intelligence in games. He has 43 refereed international publications between journal papers, conference papers, book chapters, and workshop papers.

Albert Orriols-Puig He received the M. His thesis studied how the extended classifier system XCS , one of the most influential LCS, could deal with domains that contained class imbalances. In , he was appointed as an assistant professor at the Ramon Llull University. In , he took a software engineer position at Google. His research interests include online evolutionary learning, fuzzy modeling, learning from rarities, data complexity, and machine learning in general.

He is especially interested in the application of genetic-based machine learning to real-world problems in the field of supervised and unsupervised learning. He serves as a reviewer for several conference and machine learning journals. Ryan Urbanowicz He received his B. Degree from the same institution in His masters thesis explored a ganglioside-liposome biosensor design for the detection of botulinum and cholera toxins.

In he was awarded a Dartmouth Neukom Institute Fellowship funding the development of a learning classifier system algorithm for the detection of complex multifactorial genetic associations predictive of disease. His Ph. Completion of his Ph.

D thesis is anticipated to occur in early His research interests include the development of learning classifier systems LCSs and other kinds of evolutionary learning for application to problems in genetic epidemiology. More generally, his interests extend to genetics, epidemiology, bioinformatics, artificial intelligence, data mining, and evolutionary algorithms. Music provides a perfect area of research for Evolutionary Computation. A number of problems are present and still open to new proposals, such as:.

ECMusic , the 2nd workshop on Evolutionary Computation and music, aims particularly at providing a place -both physical and virtual- where the research is not only shown but also performed. Authors will thus be encouraged to send both regular papers describing new approaches and results, along with audio records allowing to appreciate the quality of the works. A CD will be compiled and distributed F. He has co-edited several special Issues dealing with Parallel and Distributed Bioinspired Algorithms and the book entitled Parallel and Distributed Computational Intelligence, Springer, He has published more than peer reviewed papers in conferences, books and journals.

Evolutionary computation EC methods are applied in many different domains. Therefore soundly engineered, reusable, flexible, user-friendly, and interoperable software systems are more than ever required to bridge the gap between theoretical research and practical application. However, due to the heterogeneity of the application domains and the large number of EC methods, the development of such systems is both, time consuming and complex.

Consequently many EC researchers still implement individual and highly specialized software which is often developed from scratch, concentrates on a specific research question, and does not follow state of the art software engineering practices. By this means the chance to reuse existing systems and to provide systems for others to build their work on is not sufficiently seized within the EC community. In many cases the developed systems are not even publicly released, which makes the comparability and traceability of research results very hard.

This workshop enables EC researchers to exchange their ideas on how to develop and apply generic and reusable EC software systems and to present open and freely available solutions on which others can build their work on. Furthermore, the workshop should help to identify common efforts in the development of EC software systems and should highlight cooperation potentials and synergies between different research groups. It concentrates on the importance of high-quality software systems and professional software engineering in the field of EC and provides a platform for EC researchers to discuss the following and other related topics:.

From to he worked as an associate professor for software project engineering and since as a full professor for complex software systems at the University of Applied Sciences Upper Austria, Campus Hagenberg, Austria. Wagner is one of the founders of the research group Heuristic and Evolutionary Algorithms Laboratory HEAL and is the project manager and head developer of the HeuristicLab optimization environment. Michael Affenzeller He has published several papers, journal articles and books dealing with theoretical and practical aspects of evolutionary computation, genetic algorithms, and meta-heuristics in general.

In he received his PhD in engineering sciences and in he received his habilitation in applied systems engineering, both from the Johannes Kepler University Linz, Austria. The workshop will provide an opportunity for undergraduate students to present their research in evolutionary computation. Typically, presentations will describe senior-level research projects supervised by a faculty mentor; however, summer research projects or exceptional course projects may also be appropriate.

The workshop will be a half-day event, during which approximately eight undergraduate students will present their work to each other, to participating students' faculty mentors, and to GECCO participants interested in undergraduate research. Students should plan on minute presentations, followed by five minutes of questions and discussion.

Students invited to the workshop will also participate in the conference poster session. Students will display posters summarizing their work, allowing the larger GECCO community to see what's being done by undergraduates in evolutionary computation. The poster session will also be a great opportunity for networking! The goals of the Undergraduate Student Workshop are to:. Sherri Goings She has been an assistant professor at Carleton College since She received her B. Her research interests are in the fields of evolutionary computation and artificial life.

She specifically seeks to understand the evolutionary theories behind cooperation and altruism and to apply that knowledge to creating evolutionary algorithms that encourage individuals to cooperatively solve problems. Visualisation is a crucial tool in this area, and particular topics of interest are:. As well as allowing us to observe how individuals interact, visualising the evolution of a synthetic genetic population over time facilitates the analysis of how individuals change during evolution, allowing the observation of undesirable traits such as premature convergence and stagnation within the population.

In addition to this, by visualising the problem landscape we can explore the distribution of solutions generated with a GEC method to ensure that the landscape has been fully explored. In the case of multi- and many-objective optimisation problems this is enhanced by the visualisation of the trade-off between objectives, a non-trivial task for problems comprising four or more objectives, in which it is necessary to provide an intuitive visualisation of the Pareto front to a decision maker.

All of these areas draw together in the field of interactive evolutionary computation, in which it is vital that a decision maker be provided with as much information as they require to interact with the GEC method in the most efficient way possible, order to generate and understand good solutions quickly. In addition to visualising the solutions generated by a GEC process, we can also visualise the processes themselves.

It can be useful, for example, to investigate which evolutionary operators are most commonly applied by an algorithm, as well as how they are applied, in order to gain an understanding of how the process can be most effectively tuned to solve the problem at hand. GEC methods have also recently been applied to the visualisation of data.

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As the amount of data available in areas such as bioinformatics increases rapidly, it is necessary to develop methods which can visualise large quantities of data; evolutionary methods can, and have, been used for this. Work on visualising the results of evolutionary data mining is also now appearing in the literature. All of these methods benefit greatly from developments in high-powered graphics cards and work on 3D visualisation, largely driven by the computer games community.

A workshop provides a good environment for the demonstration of such methods. Based on these areas of interest the target audience for VizGEC is broad. We anticipate that people engaged in visualisation research will be interested, in addition to people from the GEC community who may be interested in using visualisation to advance their own work. We hope to attract both experienced practitioners as well as providing an introduction for those new to visualisation in GEC. He worked at Brown and Yale Universities on fluid mechanics and data analysis problems until moving to Rockefeller University, New York to work on optical imaging and modelling of the visual cortex.

His research interests lie statistical pattern recognition, multi-objective optimisation and the links between them. Recent interests include the optimisation of the performance of classifiers, which can be viewed as a many-objective optimisation problem requiring novel methods for visualisation [1,2]. Research on the construction of league tables has led to publications exploring the multi-objective nature and methods of visualising league tables [3,4].

He has held postdoctural positions as a research fellow working on the interface of Bayesian modelling and optimisation and as a business fellow focusing on knowledge transfer to industry prior to his appointment to an academic position at Exeter. His research interests include multi- and many-objective optimisation, machine learning and statistical pattern recognition and the interface between these areas. Work in these fields has led to an interest in visualisation, which in turn has led to peer reviewed work on the application and comparison of existing visualisation techniques to new domains e.

He has been active within the evolutionary computation community as a reviewer and program committee member since David Walker He is currently completing his Ph. A principal component of his thesis involves visualising such populations and he is particularly interested in how evolutionary algorithms can be used to enhance visualisation methods [3]. More recently, his research has also investigated evolutionary methods for the data mining of many-objective populations [4].

His general research interests include evolutionary problem solving, techniques for identifying preference information in data and visualisation methods. Walker, R. Everson and J. EC is a learning technique by which a population of individual agents adapts according to the selection pressures exerted by an environment; MASS seeks to understand how the actions of a population of autonomous agents can be coordinated so that some outcome is achieved, or so that some aspect of a phenomenon is elucidated through modeling.

For example, some aspects of multi-agent system engineering e. Similarly, most work in EC is concerned with how to engineer selective pressures to effectively drive the evolution of individuals towards some desired goal. Multi-agent simulation also called agent-based modeling addresses the bottom-up issue of how collective behavior emerges from individual action. Likewise, the study of evolutionary dynamics within EC often considers how population-level phenomena emerge from individual-level interactions. It is therefore natural to consider how EC may be relevant to MASS, and vice versa; indeed, applications and techniques from one field have often made use of technologies and algorithms from the other field.

His dissertation work focussed on the use of evolutionary algorithms to explore the effects of varying parameters in multi-agent simulations, and he has published on this topic at venues such as GECCO, AAMAS, and the AAAI fall symposium, and has also authored an open-source software package for performing this task.

Forrest has also combined multi-agent systems with evolutionary computation in several earlier publications, including an agent-based model that used restrictive breeding networks for an evolutionary algorithm, and a novel network-based GA crossover operator inspired by a simple agent-based diffusion mechanism. In addition, Forrest has published on the evolution of rules for non-uniform cellular automata and the analysis of noisy fitness landscapes.

Forrest's substantial experience with multi-agent simulation stems from his work at the Center for Connected Learning and Computer-Based Modeling at Northwestern University and his work contributing to the development of the NetLogo multi-agent modeling language and environment. He has been involved in a variety of agent-based modeling projects in application areas such as urban development modeling land usage and linguistics language cascades in social networks.

Forrest's other scholarly interests include studying dynamic processes on networks, emergence in complex adaptive systems, and computer science education. His research interests include evolutionary algorithms in theory and in models of complex adaptive systems, and agent-based modeling approaches to studying problems across a wide variety of complex systems, e.

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The Workshop focuses on the application of genetic and evolutionary computation GEC to problems in medicine and healthcare. Subjects will include but are not limited to applications of GEC to:. Although the application of GEC to medicine is not new, the reporting of new work tends to be distributed among various technical and clinical conferences in a somewhat disparate manner.

A dedicated workshop at GECCO provides a much needed focus for medical related applications of EC, not only providing a clear definition of the state of the art, but also support to practitioners for whom GEC might not be their main area of expertise or experience. GECCO is widely regarded to be the most authoritative conference in GEC and, as such, represents an ideal home for this important and growing community. The importance of this application area has been confirmed by authors of accepted papers from MedGEC being invited to submit extended manuscripts for inclusion in special issues of "Genetic Programming and Evolvable Machines" and "Journal of Artificial Evolution and Applications" which have now been published.

Finally, a chapter on medical applications in a book on Cartesian Genetic Programming was published by Springer last month. Stephen L. Steve's main research interests are in developing novel representations of evolutionary algorithms particularly with application to problems in medicine. His work is currently centered on the diagnosis of neurological dysfunction and analysis of mammograms.

Genetic and Evolutionary Computation: Medical Applications - Semantic Scholar

They are also guest editors for a special issue of Genetic Programming and Evolvable Machines Springer on medical applications and editors of a book on the subject John Wiley, November Hes is also a member of the editorial board for the International Journal of Computers in Healthcare. Steve has some 75 refereed publications, is a Chartered Engineer and a fellow of the British Computer Society.

Stefano Cagnoni He graduated in Electronic Engineering at the University of Florence in where he has been a PhD student and a post-doc until Since he has been with the University of Parma, where he has been Associate Professor since Recent research grants regard: co-management of a project funded by Italian Railway Network Society RFI aimed at developing an automatic inspection system for train pantographs; a "Marie Curie Initial Training Network" grant, for a four-year research training project in Medical Imaging using Bio-Inspired and Soft Computing; a grant from "Compagnia di S.

Paolo" on "Bioinformatic and experimental dissection of the signalling pathways underlying dendritic spine function". He has been Editor-in-chief of the "Journal of Artificial Evolution and Applications" from to Since , he has been chairman of EvoIASP, an event dedicated to evolutionary computation for image analysis and signal processing.

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Co-editor of special issues of journals dedicated to Evolutionary Computation for Image Analysis and Signal Processing. He has been reviewer for international journals and member of the committees of several conferences. He has been recently awarded the "Evostar Award", in recognition of the most outstanding contribution to Evolutionary Computation. Robert M. Patton He received his Ph. Patton primary research interests include data and event analytics, intelligent agents, computational intelligence, and nature-inspired computing. He currently is investigating novel approaches of evolutionary computation to the analysis of mammograms, abdominal aortic aneurysms, and traumatic brain injuries.

Although most of the evolutionary computation techniques are designed to generate specific solutions to a given instance of a problem, some of these techniques can be explored to solve more generic problems. For instance, while there are many examples of evolutionary algorithms for evolving classification models in data mining or machine learning, the work described in [1] used a genetic programming algorithm to create a generic classification algorithm which will, in turn, generate a specific classification model for any given classification dataset, in any given application domain.

Hyper-heuristics are search methods that automatically select and combine simpler heuristics, creating a generic heuristic that is used to solve any instance of a given target type of optimization problem. Hence, hyper-heuristics search in the space of heuristics, instead of searching in the problem solution space [2], raising the level of generality of the solutions produced by the hyper-heuristic evolutionary algorithm.

Post-proceedings of the papers accepted for the workshop are published - after an additional selection - as a special issue of the Springer journal Evolutionary Intelligence, which is an extra element of interest for participating in the workshop. In he received the Ph. His research interests include machine learning, evolutionary computation, and computational intelligence in games.

He has 43 refereed international publications between journal papers, conference papers, book chapters, and workshop papers. Albert Orriols-Puig He received the M. His thesis studied how the extended classifier system XCS , one of the most influential LCS, could deal with domains that contained class imbalances.

In , he was appointed as an assistant professor at the Ramon Llull University. In , he took a software engineer position at Google. His research interests include online evolutionary learning, fuzzy modeling, learning from rarities, data complexity, and machine learning in general. He is especially interested in the application of genetic-based machine learning to real-world problems in the field of supervised and unsupervised learning. He serves as a reviewer for several conference and machine learning journals. Ryan Urbanowicz He received his B. Degree from the same institution in His masters thesis explored a ganglioside-liposome biosensor design for the detection of botulinum and cholera toxins.

In he was awarded a Dartmouth Neukom Institute Fellowship funding the development of a learning classifier system algorithm for the detection of complex multifactorial genetic associations predictive of disease. His Ph. Completion of his Ph. D thesis is anticipated to occur in early His research interests include the development of learning classifier systems LCSs and other kinds of evolutionary learning for application to problems in genetic epidemiology.

More generally, his interests extend to genetics, epidemiology, bioinformatics, artificial intelligence, data mining, and evolutionary algorithms. Music provides a perfect area of research for Evolutionary Computation. A number of problems are present and still open to new proposals, such as:. ECMusic , the 2nd workshop on Evolutionary Computation and music, aims particularly at providing a place -both physical and virtual- where the research is not only shown but also performed. Authors will thus be encouraged to send both regular papers describing new approaches and results, along with audio records allowing to appreciate the quality of the works.

A CD will be compiled and distributed F. He has co-edited several special Issues dealing with Parallel and Distributed Bioinspired Algorithms and the book entitled Parallel and Distributed Computational Intelligence, Springer, He has published more than peer reviewed papers in conferences, books and journals. Evolutionary computation EC methods are applied in many different domains. Therefore soundly engineered, reusable, flexible, user-friendly, and interoperable software systems are more than ever required to bridge the gap between theoretical research and practical application.

Evolution experiment. Genetic algorithms selecting music

However, due to the heterogeneity of the application domains and the large number of EC methods, the development of such systems is both, time consuming and complex. Consequently many EC researchers still implement individual and highly specialized software which is often developed from scratch, concentrates on a specific research question, and does not follow state of the art software engineering practices.

By this means the chance to reuse existing systems and to provide systems for others to build their work on is not sufficiently seized within the EC community. In many cases the developed systems are not even publicly released, which makes the comparability and traceability of research results very hard. This workshop enables EC researchers to exchange their ideas on how to develop and apply generic and reusable EC software systems and to present open and freely available solutions on which others can build their work on. Furthermore, the workshop should help to identify common efforts in the development of EC software systems and should highlight cooperation potentials and synergies between different research groups.

It concentrates on the importance of high-quality software systems and professional software engineering in the field of EC and provides a platform for EC researchers to discuss the following and other related topics:. From to he worked as an associate professor for software project engineering and since as a full professor for complex software systems at the University of Applied Sciences Upper Austria, Campus Hagenberg, Austria.

Evostar 2018

Wagner is one of the founders of the research group Heuristic and Evolutionary Algorithms Laboratory HEAL and is the project manager and head developer of the HeuristicLab optimization environment. Michael Affenzeller He has published several papers, journal articles and books dealing with theoretical and practical aspects of evolutionary computation, genetic algorithms, and meta-heuristics in general.

In he received his PhD in engineering sciences and in he received his habilitation in applied systems engineering, both from the Johannes Kepler University Linz, Austria. The workshop will provide an opportunity for undergraduate students to present their research in evolutionary computation. Typically, presentations will describe senior-level research projects supervised by a faculty mentor; however, summer research projects or exceptional course projects may also be appropriate. The workshop will be a half-day event, during which approximately eight undergraduate students will present their work to each other, to participating students' faculty mentors, and to GECCO participants interested in undergraduate research.

Students should plan on minute presentations, followed by five minutes of questions and discussion. Students invited to the workshop will also participate in the conference poster session. Students will display posters summarizing their work, allowing the larger GECCO community to see what's being done by undergraduates in evolutionary computation. The poster session will also be a great opportunity for networking! The goals of the Undergraduate Student Workshop are to:. Sherri Goings She has been an assistant professor at Carleton College since She received her B. Her research interests are in the fields of evolutionary computation and artificial life.

She specifically seeks to understand the evolutionary theories behind cooperation and altruism and to apply that knowledge to creating evolutionary algorithms that encourage individuals to cooperatively solve problems. Visualisation is a crucial tool in this area, and particular topics of interest are:. As well as allowing us to observe how individuals interact, visualising the evolution of a synthetic genetic population over time facilitates the analysis of how individuals change during evolution, allowing the observation of undesirable traits such as premature convergence and stagnation within the population.

In addition to this, by visualising the problem landscape we can explore the distribution of solutions generated with a GEC method to ensure that the landscape has been fully explored. In the case of multi- and many-objective optimisation problems this is enhanced by the visualisation of the trade-off between objectives, a non-trivial task for problems comprising four or more objectives, in which it is necessary to provide an intuitive visualisation of the Pareto front to a decision maker.

All of these areas draw together in the field of interactive evolutionary computation, in which it is vital that a decision maker be provided with as much information as they require to interact with the GEC method in the most efficient way possible, order to generate and understand good solutions quickly. In addition to visualising the solutions generated by a GEC process, we can also visualise the processes themselves. It can be useful, for example, to investigate which evolutionary operators are most commonly applied by an algorithm, as well as how they are applied, in order to gain an understanding of how the process can be most effectively tuned to solve the problem at hand.

GEC methods have also recently been applied to the visualisation of data.

As the amount of data available in areas such as bioinformatics increases rapidly, it is necessary to develop methods which can visualise large quantities of data; evolutionary methods can, and have, been used for this. Work on visualising the results of evolutionary data mining is also now appearing in the literature. All of these methods benefit greatly from developments in high-powered graphics cards and work on 3D visualisation, largely driven by the computer games community. A workshop provides a good environment for the demonstration of such methods. Based on these areas of interest the target audience for VizGEC is broad.

We anticipate that people engaged in visualisation research will be interested, in addition to people from the GEC community who may be interested in using visualisation to advance their own work. We hope to attract both experienced practitioners as well as providing an introduction for those new to visualisation in GEC. He worked at Brown and Yale Universities on fluid mechanics and data analysis problems until moving to Rockefeller University, New York to work on optical imaging and modelling of the visual cortex.

His research interests lie statistical pattern recognition, multi-objective optimisation and the links between them. Recent interests include the optimisation of the performance of classifiers, which can be viewed as a many-objective optimisation problem requiring novel methods for visualisation [1,2]. Research on the construction of league tables has led to publications exploring the multi-objective nature and methods of visualising league tables [3,4].

He has held postdoctural positions as a research fellow working on the interface of Bayesian modelling and optimisation and as a business fellow focusing on knowledge transfer to industry prior to his appointment to an academic position at Exeter. His research interests include multi- and many-objective optimisation, machine learning and statistical pattern recognition and the interface between these areas.

Work in these fields has led to an interest in visualisation, which in turn has led to peer reviewed work on the application and comparison of existing visualisation techniques to new domains e. He has been active within the evolutionary computation community as a reviewer and program committee member since David Walker He is currently completing his Ph. A principal component of his thesis involves visualising such populations and he is particularly interested in how evolutionary algorithms can be used to enhance visualisation methods [3].

More recently, his research has also investigated evolutionary methods for the data mining of many-objective populations [4]. His general research interests include evolutionary problem solving, techniques for identifying preference information in data and visualisation methods. Walker, R.

Genetic Algorithm-Evolved Bayesian Network Classifier for Medical Applications

Everson and J. EC is a learning technique by which a population of individual agents adapts according to the selection pressures exerted by an environment; MASS seeks to understand how the actions of a population of autonomous agents can be coordinated so that some outcome is achieved, or so that some aspect of a phenomenon is elucidated through modeling. For example, some aspects of multi-agent system engineering e.

Similarly, most work in EC is concerned with how to engineer selective pressures to effectively drive the evolution of individuals towards some desired goal. Multi-agent simulation also called agent-based modeling addresses the bottom-up issue of how collective behavior emerges from individual action.

Likewise, the study of evolutionary dynamics within EC often considers how population-level phenomena emerge from individual-level interactions. It is therefore natural to consider how EC may be relevant to MASS, and vice versa; indeed, applications and techniques from one field have often made use of technologies and algorithms from the other field. His dissertation work focussed on the use of evolutionary algorithms to explore the effects of varying parameters in multi-agent simulations, and he has published on this topic at venues such as GECCO, AAMAS, and the AAAI fall symposium, and has also authored an open-source software package for performing this task.

Forrest has also combined multi-agent systems with evolutionary computation in several earlier publications, including an agent-based model that used restrictive breeding networks for an evolutionary algorithm, and a novel network-based GA crossover operator inspired by a simple agent-based diffusion mechanism. In addition, Forrest has published on the evolution of rules for non-uniform cellular automata and the analysis of noisy fitness landscapes.

Forrest's substantial experience with multi-agent simulation stems from his work at the Center for Connected Learning and Computer-Based Modeling at Northwestern University and his work contributing to the development of the NetLogo multi-agent modeling language and environment.

He has been involved in a variety of agent-based modeling projects in application areas such as urban development modeling land usage and linguistics language cascades in social networks. Forrest's other scholarly interests include studying dynamic processes on networks, emergence in complex adaptive systems, and computer science education.

His research interests include evolutionary algorithms in theory and in models of complex adaptive systems, and agent-based modeling approaches to studying problems across a wide variety of complex systems, e. The Workshop focuses on the application of genetic and evolutionary computation GEC to problems in medicine and healthcare. Subjects will include but are not limited to applications of GEC to:. Although the application of GEC to medicine is not new, the reporting of new work tends to be distributed among various technical and clinical conferences in a somewhat disparate manner.

A dedicated workshop at GECCO provides a much needed focus for medical related applications of EC, not only providing a clear definition of the state of the art, but also support to practitioners for whom GEC might not be their main area of expertise or experience. GECCO is widely regarded to be the most authoritative conference in GEC and, as such, represents an ideal home for this important and growing community.

The importance of this application area has been confirmed by authors of accepted papers from MedGEC being invited to submit extended manuscripts for inclusion in special issues of "Genetic Programming and Evolvable Machines" and "Journal of Artificial Evolution and Applications" which have now been published. Finally, a chapter on medical applications in a book on Cartesian Genetic Programming was published by Springer last month.

Stephen L. Steve's main research interests are in developing novel representations of evolutionary algorithms particularly with application to problems in medicine. His work is currently centered on the diagnosis of neurological dysfunction and analysis of mammograms. They are also guest editors for a special issue of Genetic Programming and Evolvable Machines Springer on medical applications and editors of a book on the subject John Wiley, November Hes is also a member of the editorial board for the International Journal of Computers in Healthcare.

Steve has some 75 refereed publications, is a Chartered Engineer and a fellow of the British Computer Society. Stefano Cagnoni He graduated in Electronic Engineering at the University of Florence in where he has been a PhD student and a post-doc until Since he has been with the University of Parma, where he has been Associate Professor since Recent research grants regard: co-management of a project funded by Italian Railway Network Society RFI aimed at developing an automatic inspection system for train pantographs; a "Marie Curie Initial Training Network" grant, for a four-year research training project in Medical Imaging using Bio-Inspired and Soft Computing; a grant from "Compagnia di S.

Paolo" on "Bioinformatic and experimental dissection of the signalling pathways underlying dendritic spine function". He has been Editor-in-chief of the "Journal of Artificial Evolution and Applications" from to Since , he has been chairman of EvoIASP, an event dedicated to evolutionary computation for image analysis and signal processing. Co-editor of special issues of journals dedicated to Evolutionary Computation for Image Analysis and Signal Processing.

He has been reviewer for international journals and member of the committees of several conferences. He has been recently awarded the "Evostar Award", in recognition of the most outstanding contribution to Evolutionary Computation. Robert M. Patton He received his Ph. Patton primary research interests include data and event analytics, intelligent agents, computational intelligence, and nature-inspired computing. He currently is investigating novel approaches of evolutionary computation to the analysis of mammograms, abdominal aortic aneurysms, and traumatic brain injuries.

Although most of the evolutionary computation techniques are designed to generate specific solutions to a given instance of a problem, some of these techniques can be explored to solve more generic problems. For instance, while there are many examples of evolutionary algorithms for evolving classification models in data mining or machine learning, the work described in [1] used a genetic programming algorithm to create a generic classification algorithm which will, in turn, generate a specific classification model for any given classification dataset, in any given application domain.

Hyper-heuristics are search methods that automatically select and combine simpler heuristics, creating a generic heuristic that is used to solve any instance of a given target type of optimization problem. Hence, hyper-heuristics search in the space of heuristics, instead of searching in the problem solution space [2], raising the level of generality of the solutions produced by the hyper-heuristic evolutionary algorithm. For instance, a hyper-heuristic can generate a generic heuristic for solving any instance of the traveling salesman problem, involving any number of cities and any set of distances associated with those cities [3]; whilst a conventional evolutionary algorithm would just evolve a solution to one particular instance of the traveling salesman problem, involving a predefined set of cities and associated distances between them.

Whether we name it an approach for automatically designing algorithms or hyper-heuristics, in both cases, a set of human designed procedural components or heuristics surveyed from the literature are chosen as a starting point or as "building blocks" for the evolutionary search. Besides, new procedural components and heuristics can be automatically generated, depending on which components are first provided to the method.

These methods have the advantage of producing solutions that are applicable to any instance of a problem domain, instead of a solution specifically produced to a single instance of the problem. The areas of application of these methods may include, for instance, data mining, machine learning, and optimization. Pappa and A. Burke, M. Hyde, G. Kendall and J. Woodward, A genetic programming hyper-heuristic approach for evolving two dimensional strip packing heuristics. Oltean and D. Evolving TSP heuristics using multi expression programming. Springer, Gisele L.

She is the author of a research-oriented book on data mining and evolutionary algorithms, and her current research interests are on data mining, bio-inspired computational intelligence algorithms and social networks. He recently completed a post-doc at the University of Nottingham investigating the use of Genetic Programming to discover novel heuristics. His research interests include fundamental issues in Machine Learning especially Genetic Programming.

Jerry Swan Prior to obtaining a PhD in computational group theory at Nottingham, Jerry Swan has spent 20 years in industry as a software developer. He was the owner of a computer games company for most of the s and has worked in areas as diverse as logistics and generative music. His research interests include hyper-heuristics, symbolic computation and machine learning. His research interests include evolutionary computation, hyper-heuristics, metaheuristics, and operational research. Global increases in living standards, diminishing natural resources and environmental concerns place energy amongst the most important global issues today.

The management, control and planning of, and efficient use of energy in future energy systems brings about many important challenges. Energy systems are not only real-world systems, they are also one of the most important foundations of the modern world. In many situations therefore, problem-specific algorithms are infeasible or impractical. Typical real-world challenges that are addressed by EAs are of the optimization type. The workshop covers all energy-related applications of evolutionary computation, including but not limited to:.

Both theoretical papers and papers describing practical experiences are welcome. Alexandru-Adrian Tantar He received his Ph.