Searching the Hyper-heuristic Design Space

We extend a previous mathematical formulation of hyper-heuristics to reflect the emerging generalization of the concept. We show that this leads naturally to a recursive definition of hyper-heuristics and to a division of responsibility that is suggestive of a blackboard architecture, in which individual heuristics annotate a shared workspace with information that may also be exploited by other heuristics. Such a framework invites consideration of the kind of relaxations of the domain barrier that can be achieved without loss of generality. We give a concrete example of this architecture with an application to the 3-SAT domain that significantly improves on a related token-ring hyper-heuristic.

[1]  Victor Lesser,et al.  The Evolution of Blackboard Control Architectures , 1992 .

[2]  Terry Jones,et al.  Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms , 1995, ICGA.

[3]  P. Stadler Landscapes and their correlation functions , 1996 .

[4]  Matthias Fuchs,et al.  High Performance ATP Systems by Combining Several AI Methods , 1997, IJCAI.

[5]  Fred W. Glover,et al.  Tabu Search - Part I , 1989, INFORMS J. Comput..

[6]  Mohammad Majid al-Rifaie,et al.  Creativity and Autonomy in Swarm Intelligence Systems , 2012, Cognitive Computation.

[7]  Graham Kendall,et al.  Exploring Hyper-heuristic Methodologies with Genetic Programming , 2009 .

[8]  Yan Su,et al.  Imperfect Evolutionary Systems , 2007, IEEE Transactions on Evolutionary Computation.

[9]  P. W. Jones,et al.  Bandit Problems, Sequential Allocation of Experiments , 1987 .

[10]  Barbara Hayes-Roth,et al.  A Blackboard Architecture for Control , 1985, Artif. Intell..

[11]  Colin R. Reeves Fitness Landscapes and Evolutionary Algorithms , 1999, Artificial Evolution.

[12]  Kevin Leyton-Brown,et al.  SATzilla: Portfolio-based Algorithm Selection for SAT , 2008, J. Artif. Intell. Res..

[13]  Sanja Petrovic,et al.  Case-based heuristic selection for timetabling problems , 2006, J. Sched..

[14]  Graham Kendall,et al.  A Tabu-Search Hyperheuristic for Timetabling and Rostering , 2003, J. Heuristics.

[15]  J. Mark Bishop,et al.  Computational Creativity, Intelligence and Autonomy , 2012, Cognitive Computation.

[16]  Fred Glover,et al.  Tabu Search - Part II , 1989, INFORMS J. Comput..

[17]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[18]  C. Reeves,et al.  Properties of fitness functions and search landscapes , 2001 .

[19]  Michael Luck,et al.  Creativity Through Autonomy and Interaction , 2012, Cognitive Computation.

[20]  Peter A. N. Bosman,et al.  Proceedings of the Genetic and Evolutionary Computation Conference - GECCO - 2006 , 2006 .

[21]  Saso Dzeroski,et al.  Discovering dynamics: From inductive logic programming to machine discovery , 1993, Journal of Intelligent Information Systems.

[22]  Luca Di Gaspero,et al.  EASYLOCAL++: an object‐oriented framework for the flexible design of local‐search algorithms , 2003, Softw. Pract. Exp..

[23]  Sanja Petrovic,et al.  A cooperative hyper-heuristic search framework , 2010, J. Heuristics.

[24]  Thomas Stützle,et al.  SATLIB: An Online Resource for Research on SAT , 2000 .

[25]  A. E. Eiben,et al.  Self-adaptivity for constraint satisfaction: learning penalty functions , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[26]  Andrea Roli,et al.  MAGMA: a multiagent architecture for metaheuristics , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[27]  Grady Booch,et al.  Object-oriented analysis and design with applications, third edition , 2007, SOEN.

[28]  Peyton Jones,et al.  Haskell 98 language and libraries : the revised report , 2003 .

[29]  R. A. Brooks,et al.  Intelligence without Representation , 1991, Artif. Intell..

[30]  Peter F. Stadler,et al.  Towards a theory of landscapes , 1995 .

[31]  Thomas Stützle,et al.  A Racing Algorithm for Configuring Metaheuristics , 2002, GECCO.

[32]  Andy Oram,et al.  Beautiful Code: Leading Programmers Explain How They Think (Theory in Practice (O'Reilly)) , 2007 .

[33]  Wim Hordijk,et al.  A Measure of Landscapes , 1996, Evolutionary Computation.

[34]  Javier G. Marín-Blázquez,et al.  A Hyper-Heuristic Framework with XCS: Learning to Create Novel Problem-Solving Algorithms Constructed from Simpler Algorithmic Ingredients , 2005, IWLCS.

[35]  Andrew W. Moore,et al.  Reinforcement Learning: A Survey , 1996, J. Artif. Intell. Res..

[36]  Graham Kendall,et al.  Hyperion - A Recursive Hyper-Heuristic Framework , 2011, LION.

[37]  Steve R. White,et al.  Concepts of scale in simulated annealing , 2008 .

[38]  Roberto Battiti,et al.  The Reactive Tabu Search , 1994, INFORMS J. Comput..

[39]  Graham Kendall,et al.  Hyper-Heuristics: An Emerging Direction in Modern Search Technology , 2003, Handbook of Metaheuristics.

[40]  Peter I. Cowling,et al.  Hyperheuristics for managing a large collection of low level heuristics to schedule personnel , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[41]  Graham Kendall,et al.  A Classification of Hyper-heuristic Approaches , 2010 .

[42]  E. Weinberger,et al.  Correlated and uncorrelated fitness landscapes and how to tell the difference , 1990, Biological Cybernetics.

[43]  Raymond S. K. Kwan,et al.  Distributed Choice Function Hyper-heuristics for Timetabling and Scheduling , 2004, PATAT.

[44]  C. R. Reeves,et al.  Landscapes, operators and heuristic search , 1999, Ann. Oper. Res..

[45]  Graham Kendall,et al.  A Hyperheuristic Approach to Scheduling a Sales Summit , 2000, PATAT.

[46]  Grady Booch,et al.  Object Oriented Analysis And Design With Applications 3Rd Edition , 2009 .