Algorithm Selection on Adaptive Operator Selection: A Case Study on Genetic Algorithms

[1]  Yuanyuan Zhang,et al.  Search-based software engineering: Trends, techniques and applications , 2012, CSUR.

[2]  Barry O'Sullivan,et al.  Evolving Instance Specific Algorithm Configuration , 2021, SOCS.

[3]  Zhi Yuan,et al.  Reinforcement learning for adaptive operator selection in memetic search applied to quadratic assignment problem , 2014, GECCO.

[4]  E. Lawler The Quadratic Assignment Problem , 1963 .

[5]  M. A. L. Thathachar,et al.  Networks of Learning Automata , 2004 .

[6]  Heike Trautmann,et al.  Automated Algorithm Selection: Survey and Perspectives , 2018, Evolutionary Computation.

[7]  E. S. Page CONTINUOUS INSPECTION SCHEMES , 1954 .

[8]  Hoong Chuin Lau,et al.  Self-organizing Neural Network for Adaptive Operator Selection in Evolutionary Search , 2016, LION.

[9]  Panos M. Pardalos,et al.  Quadratic Assignment Problem , 1997, Encyclopedia of Optimization.

[10]  Michèle Sebag,et al.  Dynamic Multi-Armed Bandits and Extreme Value-Based Rewards for Adaptive Operator Selection in Evolutionary Algorithms , 2009, LION.

[11]  Yuri Malitsky,et al.  Parallel SAT Solver Selection and Scheduling , 2012, CP.

[12]  Yuri Malitsky,et al.  Algorithm Selection and Scheduling , 2011, CP.

[13]  Wali Khan Mashwani,et al.  Hybrid adaptive evolutionary algorithm based on decomposition , 2017, Appl. Soft Comput..

[14]  Gabriela Ochoa,et al.  Evolvability metrics in adaptive operator selection , 2014, GECCO.

[15]  Aurora Trinidad Ramirez Pozo,et al.  Deriving products for variability test of Feature Models with a hyper-heuristic approach , 2016, Appl. Soft Comput..

[16]  Ruhul A. Sarker,et al.  Landscape-based adaptive operator selection mechanism for differential evolution , 2017, Inf. Sci..

[17]  Dirk Thierens,et al.  Adaptive Strategies for Operator Allocation , 2007, Parameter Setting in Evolutionary Algorithms.

[18]  David H. Wolpert,et al.  No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..

[19]  Michèle Sebag,et al.  Adaptive operator selection with dynamic multi-armed bandits , 2008, GECCO '08.

[20]  Patrick De Causmaecker,et al.  An Intelligent Hyper-Heuristic Framework for CHeSC 2011 , 2012, LION.

[21]  Frédéric Saubion,et al.  Non stationary operator selection with island models , 2013, GECCO '13.

[22]  Frédéric Saubion,et al.  Simulating non-stationary operators in search algorithms , 2016, Appl. Soft Comput..

[23]  Patrick De Causmaecker,et al.  A new hyper-heuristic as a general problem solver: an implementation in HyFlex , 2013, J. Sched..

[24]  Hoong Chuin Lau,et al.  OSCAR: Online Selection of Algorithm Portfolios with Case Study on Memetic Algorithms , 2015, LION.

[25]  Carolina P. de Almeida,et al.  Adaptive Operator Selection for Many-Objective Optimization with NSGA-III , 2017, EMO.

[26]  Bernd Bischl,et al.  ASlib: A benchmark library for algorithm selection , 2015, Artif. Intell..

[27]  Wali Khan Mashwani,et al.  Hybrid non-dominated sorting genetic algorithm with adaptive operators selection , 2017, Appl. Soft Comput..

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

[29]  Michel Gendreau,et al.  Hyper-heuristics: a survey of the state of the art , 2013, J. Oper. Res. Soc..

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

[31]  Michèle Sebag,et al.  Extreme Value Based Adaptive Operator Selection , 2008, PPSN.

[32]  Dirk Thierens,et al.  An Adaptive Pursuit Strategy for Allocating Operator Probabilities , 2005, BNAIC.

[33]  Predrag Janicic,et al.  Simple algorithm portfolio for SAT , 2011, Artificial Intelligence Review.

[34]  Marius Lindauer,et al.  An Empirical Study of Per-instance Algorithm Scheduling , 2016, LION.

[35]  Michèle Sebag,et al.  Toward comparison-based adaptive operator selection , 2010, GECCO '10.

[36]  Mustafa Mısır,et al.  Hyper-heuristics: Autonomous Problem Solvers , 2021, Automated Design of Machine Learning and Search Algorithms.

[37]  Mustafa Misir,et al.  Matrix Factorization Based Benchmark Set Analysis: A Case Study on HyFlex , 2017, SEAL.

[38]  Hoong Chuin Lau,et al.  ADVISER: A Web-Based Algorithm Portfolio Deviser , 2015, LION.

[39]  Hoong Chuin Lau,et al.  Designing and Comparing Multiple Portfolios of Parameter Configurations for Online Algorithm Selection , 2016, LION.

[40]  Michèle Sebag,et al.  Alors: An algorithm recommender system , 2017, Artif. Intell..

[41]  Marius Thomas Lindauer,et al.  AutoFolio: An Automatically Configured Algorithm Selector , 2015, J. Artif. Intell. Res..

[42]  Hongbin Dong,et al.  Pure Strategy or Mixed Strategy? - An Initial Comparison of Their Asymptotic Convergence Rate and Asymptotic Hitting Time , 2011, EvoCOP.

[43]  Michèle Sebag,et al.  Analyzing bandit-based adaptive operator selection mechanisms , 2010, Annals of Mathematics and Artificial Intelligence.

[44]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[45]  Thomas Stützle,et al.  Off-line and On-line Tuning: A Study on Operator Selection for a Memetic Algorithm Applied to the QAP , 2011, EvoCOP.

[46]  Predrag Janicic,et al.  Instance-Based Selection of Policies for SAT Solvers , 2009, SAT.

[47]  Barry O'Sullivan,et al.  SNNAP: Solver-Based Nearest Neighbor for Algorithm Portfolios , 2013, ECML/PKDD.

[48]  Frédéric Saubion,et al.  A dynamic island model for adaptive operator selection , 2012, GECCO '12.

[49]  H. Robbins,et al.  Asymptotically efficient adaptive allocation rules , 1985 .