|Speaker: Lee Spector
Short Bio: Lee Spector is a Professor of Computer Science in the School of Cognitive Science at Hampshire College and an Adjunct Professor in the Department of Computer Science at the University of Massachusetts, Amherst. He received a B.A. in Philosophy from Oberlin College in 1984, and a Ph.D. from the Department of Computer Science at the University of Maryland in 1992. At Hampshire he has held the MacArthur Chair and served as the elected faculty member of the Board of Trustees, the Dean of the School of Cognitive Science, and Co-Director of the Design, Art and Technology (DART) program. He directs the Hampshire College Computational Intelligence Laboratory and supervises the Hampshire College Cluster Computing Facility.
The main objective of this workshop is to discuss hyper-heuristics and related methods, including but not limited to evolutionary computation methods, for generating and improving algorithms with the goal of producing solutions (algorithms) that are applicable to multiple instances of a problem domain. The areas of application of these methods include optimization, data mining and machine learning [1-18].
Automatically generating and improving algorithms by means of other algorithms has been the goal of several research fields, including Artificial Intelligence in the early 1950s, Genetic Programming in the early 1990s, and more recently automated algorithm configuration  and hyper-heuristics . The term hyper-heuristics generally describes meta-heuristics applied to a space of algorithms. While Genetic Programming has most famously been used to this end, other evolutionary algorithms and meta-heuristics have successfully been used to automatically design novel (components of) algorithms. Automated algorithm configuration grew from the necessity of tuning the parameter settings of meta-heuristics and it has produced several powerful (hyper-heuristic) methods capable of designing new algorithms by either selecting components from a flexible algorithmic framework [3,4] or recombining them following a grammar description [5,9].
Although most Evolutionary Computation techniques are designed to generate specific solutions to a given instance of a problem, one of the defining goals of hyper-heuristics is to produce solutions that solve more generic problems. For instance, while there are many examples of Evolutionary Algorithms for evolving classification models in data mining and machine learning, the work described in  employed a hyper-heuristic using Genetic Programming to create a generic classification algorithm which in turn generates a specific classification model for any given classification dataset, in any given application domain. In other words, the hyper-heuristic is operating at a higher level of abstraction compared to how most search methodologies are currently employed; i.e., it is searching the space of algorithms as opposed to directly searching in the problem solution space , raising the level of generality of the solutions produced by the hyper-heuristic evolutionary algorithm. In contrast to standard Genetic Programming, which attempts to build programs from scratch from a typically small set of atomic functions, hyper-heuristic methods specify an appropriate set of primitives (e.g., algorithmic components) and allow evolution to combine them in novel ways as appropriate for the targeted problem class. While this allows searches in constrained search spaces based on problem knowledge, it does not in any way limit the generality of this approach as the primitive set can be selected to be Turing-complete. Typically, however, the initial algorithmic primitive set is composed of primitive components of existing high-performing algorithms for the problems being targeted; this more targeted approach very significantly reduces the initial search space, resulting in a practical approach rather than a mere theoretical curiosity. Iterative refining of the primitives allows for gradual and directed enlarging of the search space until convergence.
As meta-heuristics are themselves a type of algorithm, they too can be automatically designed employing hyper-heuristics. For instance, in 2007, Genetic Programming was used to evolve mate selection in evolutionary algorithms ; in 2011, Linear Genetic Programming was used to evolve crossover operators ; more recently, Genetic Programming was used to evolve complete black-box search algorithms [13,14,16]. Moreover, hyper-heuristics may be applied before deploying an algorithm (offline)  or while problems are being solved (online) , or even continuously learn by solving new problems (life-long) . Offline and life-long hyper-heuristics are particularly useful for real-world problem solving where one can afford a large amount of a priori computational time to subsequently solve many problem instances drawn from a specified problem domain, thus amortizing the a priori computational time over repeated problem solving.
Very little is known yet about the foundations of hyper-heuristics, such as the impact of the meta-heuristic exploring algorithm space on the performance of the thus automatically designed algorithm. An initial study compared the performance of algorithms generated by hyper-heuristics powered by five major types of Genetic Programming .
|March 31st 2017||Workshop paper submission deadline|
|April 15th 2017||Notification of acceptance|
|May 1st 2017||Author registration deadline|
|May 1, 2017||Camera-ready deadline|
Submit your paper in PDF format by e-mail, with subject "Submission to ECADA @GECCO2017", to all three e-mail addresses: firstname.lastname@example.org; email@example.com; firstname.lastname@example.org.
All accepted papers will be presented at the workshop and appear in the GECCO Conference Companion Proceedings.
|Manuel López-Ibáńez is a lecturer in the Decision and Cognitive Sciences Group of the Alliance Manchester Business School, University of Manchester, UK. Between October 2011 and September 2015, he was a postdoctoral researcher (Chargé de recherche) of the Belgian Fund for Scientific Research (F.R.S.-FNRS) working at the IRIDIA laboratory of Université libre de Bruxelles, Brussels, Belgium. He received the M.S. degree in computer science from the University of Granada, Granada, Spain, in 2004, and the Ph.D. degree from Edinburgh Napier University, U.K., in 2009. He has published 17 journal papers, 6 book chapters and 36 papers in peer-reviewed proceedings of international conferences on diverse topics such as evolutionary algorithms, ant colony optimization, multi-objective optimization, and various combinatorial and real-world optimization problems. His current research interests are automatic configuration and design of optimization algorithms, for single and multi-objective optimization. He is the lead developer and current maintainer of the irace automatic configuration method (http://iridia.ulb.ac.be/irace).|
E-mail: email@example.comDaniel R. Tauritz is an Associate Professor in the Department of Computer Science at the Missouri University of Science and Technology (S&T), a contract scientist for Sandia National Laboratories, a former Guest Scientist at Los Alamos National Laboratory (LANL), the founding director of S&T's Natural Computation Laboratory, and founding academic director of the LANL/S&T Cyber Security Sciences Institute. He received his Ph.D. in 2002 from Leiden University for Adaptive Information Filtering employing a novel type of evolutionary algorithm. He served previously as GECCO 2010 Late Breaking Papers Chair, GECCO 2012 & 2013 GA Track Co-Chair, GECCO 2015 ECADA Workshop Co-Chair, GECCO 2015 MetaDeeP Workshop Co-Chair, GECCO 2015 Hyper-heuristics Tutorial co-instructor, and GECCO 2015 CBBOC Competition co-organizer. For several years he has served on the GECCO GA track program committee, the Congress on Evolutionary Computation program committee, and a variety of other international conference program committees. His research interests include the design of hyper-heuristics and self-configuring evolutionary algorithms and the application of computational intelligence techniques in cyber security, critical infrastructure protection, and program understanding. He was granted a US patent for an artificially intelligent rule-based system to assist teams in becoming more effective by improving the communication process between team members.
E-mail: firstname.lastname@example.orgJohn R. Woodward is a Lecturer at the University of Stirling, within the CHORDS group and is employed on the DAASE project, and for the previous four years was a lecturer with the University of Nottingham. He holds a BSc in Theoretical Physics, an MSc in Cognitive Science and a PhD in Computer Science, all from the University of Birmingham. His research interests include Automated Software Engineering, particularly Search Based Software Engineering, Artificial Intelligence/Machine Learning and in particular Genetic Programming. He has over 50 publications in Computer Science, Operations Research and Engineering which include both theoretical and empirical contributions, and given over 100 talks at international conferences and as an invited speaker at universities. He has worked in industrial, military, educational and academic settings, and been employed by EDS, CERN and RAF and three UK Universities.