How to Solve It: Modern Heuristics

Keywords

Travelling salesman problem, Random variable, Heuristics, Mathematics, Simple (philosophy)

References

• Performance of Diploid Dominance with Genetically Synthesized Signal Processing NetworksFrank Greene — 1997
• The Breakout Method for Escaping from Local MinimaPaul Morris — 1993
• A Comparison of Genetic Sequencing OperatorsTimothy Starkweather, Susan E. McDaniel, Keith E. Mathias — 1991
• An Adaptive Crossover Distribution Mechanism for Genetic AlgorithmsJ. David Schaffer, Amy Morishima — 1987
• Bit-Climbing, Representational Bias, and Test Suite DesignLawrence M. Davis — 1991
• A PATCHWORK model for evolutionary algorithms with structured and variable size populationsThiemo Krink, Brian H. Mayoh, Zbigniew Michalewicz — 1999
• Looking Around: Using Clues from the Data Space to Guide Genetic Algorithm SearchesHugh Cartwright, Gregory F. Mott — 1991
• Genetic algorithms for function optimizationAnne Brindle — 1980
• A Note on Usefulness of Geometrical Crossover for Numerical Optimization ProblemsZbigniew Michalewicz, Girish Nazhiyath, Maciej Michalewicz — 1996
• Preliminary Investigations into a Two-State Method of Evolutionary Optimization on Constrained ProblemsHyun Myung, Jong-Hwan Kim, David B. Fogel — 1995
• Empirical Observations on the Roles of Crossover and MutationAnnie S. Wu, Robert Lindsay, Rick Riolo — 1997
• Some experiments in machine learning using vector evaluated genetic algorithms (artificial intelligence, optimization, adaptation, pattern recognition)James David Schaffer — 1984
• A dual genetic algorithm for bounded integer programs James C. Bean, Atidel Ben Hadj-Alouane.James C. Bean, Atidel B. Hadj-Alouane — 1993
• Application of Genetic Algorithms to Task Planning and LearningWilfried Jakob, M. Gorges-Schleuter, Christian Blume — 1992
• A Study of Permutation Crossover Operators on the Traveling Salesman ProblemI. M. Oliver, David J. Smith, J. R. C. Holland — 1987
• Adaptation to Changing Environments by Means of the Memory Based Thermodynamical Genetic AlgorithmNaoki Mori, Hajime Kita, Yoshikazu Nishikawa — 1997
• Domain-Independent Extensions to GSAT: Solving Large Structured Satisfiability ProblemsBart Selman, Henry Kautz — 1993
• The ARGOT Strategy: Adaptive Representation Genetic Optimizer TechniqueCraig G. Shaefer — 1987
• The Effects of Noise on Self-Adaptive Evolutionary OptimizationPeter J. Angeline — 1996
• Don't Worry, Be MessyDavid E. Goldberg, Kalyanmoy Deb, Bradley Korb — 1991

Cited by

• Metaheurísticas de optimización combinatoria: uso de Simulated Annealing para un problema de calendarizaciónPilar Moreno Díaz, Gabriel Isidoro Huecas Fernández-Toribio, Jesús Sánchez Allende — 2007
• A Tabu search approach for the nuclear magnetic resonance protein structure based assignment problemGizem Çavuşlar — 2011
• Heuristic optimisationUwe Aickelin, Alistair Clark — 2011
• On-Line Adaptation of Game Opponent AI in Simulation and in PracticePieter Spronck, I.G. Sprinkhuizen-Kuyper, Eric Postma — 2003
• Visibility problems for sensor networks and unmanned air vehiclesKarl J. Obermeyer — 2010
• Monitoring, Security, and Rescue Techniques in Multiagent Systems (Advances in Soft Computing)Barbara Dunin‐Kȩplicz, Andrzej Jankowski, Andrzej Skowron — 2005
• Constraint-Handling in Evolutionary OptimizationEfrn Mezura-Montes, Mezura-Montes, Efrén — 2009
• A simple genetic algorithm for multiple sequence alignment.Cedric Gondro, B P Kinghorn — 2007
• Distributed Community Cooperation in Multi Agent Filtering FrameworkŞahin Albayrak, Dragan Z. Milosevic — 2005
• Theoretical Framework for Cooperation and Competition in Evolutionary ComputationE. Eberbach, M. Burgin — 2016
• Thermodynamic calculations using a simulated annealing optimization algorithmAdrián Bonilla‐Petriciolet, Juan Gabriel Segovia‐Hernández, Florianne Castillo-Borja — 2007
• Vision 3D multi-images : contribution à l'obtention de solutions globales par optimisation polynomiale et théorie des moments. (Contribution to the global resolution of minimization problems in computer vision by polynomial optimization and moments theory)Florian Bugarin — 2012
• A flexible computational framework for detecting, characterizing, and interpreting statistical patterns of epistasis in genetic studies of human disease susceptibility.Michele Heide Berman — 2006
• Test case generation for testing of timeliness : Extended versionRobert Nilsson, Jeff Offutt, Jonas Mellin — 2005
• A Model to Create an Efficient and Equitable Admission Policy for Patients Arriving to the Cardiothoracic ICU*Alphia Possamai‐Inesedy — 2013
• Des modèles et des algorithmes pour la gestion des ressources dans les grilles de plusieurs organisationsKrzysztof Rzaḑca — 2008
• Improving Content-Oriented XML Retrieval by Applying Structural PatternsPhilipp Dopichaj — 2007
• Response Time Aware Coordination in Multi Agent Filtering FrameworkŞahin Albayrak, Dragan Z. Milosevic — 2005
• Using Uniform Crossover to Refine Simulated Annealing Solutions for Automatic Design of Spatial LayoutsFadratul Hafinaz Hassan, Allan Tucker — 2010
• Assessment and Redesign of the Synoptic Water Quality Monitoring Network in the Great Smoky Mountains National ParkK. R. Odom — 2003

Related papers

• Genetic Algorithms in Search Optimization and Machine LearningD. Goldberg — 1988
• Genetic AlgorithmsDavid E. Goldberg, William Shakespeare — 2002
• Tabu SearchFred Glover, Manuel Laguna — 1997
• Particle swarm optimizationJames Kennedy, R.C. Eberhart — 2002
• Genetic Algorithms in SearchD. E. Goldberg — 1989
• No free lunch theorems for optimizationDavid H. Wolpert, William G. Macready — 1997
• Genetic Algorithms + Data Structures = Evolution ProgramsZbigniew Michalewicz — 1996
• A fast and elitist multiobjective genetic algorithm: NSGA-IIKalyanmoy Deb, Amrit Pratap, Sakshi Agarwal — 2002
• Tabu Search - Part IIFred Glover — 1989
• An introduction to genetic algorithmsMelanie Mitchell — 1996