暂无分享,去创建一个
[1] Frank Neumann,et al. Maximizing Submodular Functions under Matroid Constraints by Evolutionary Algorithms , 2015, Evolutionary Computation.
[2] Frank Neumann,et al. Fixed-Parameter Evolutionary Algorithms and the Vertex Cover Problem , 2012, Algorithmica.
[3] Marco Laumanns,et al. Running time analysis of multiobjective evolutionary algorithms on pseudo-Boolean functions , 2004, IEEE Transactions on Evolutionary Computation.
[4] Chandra A. Poojari,et al. Genetic Algorithm based technique for solving Chance Constrained Problems , 2008, Eur. J. Oper. Res..
[5] Frank Neumann,et al. Expected runtimes of a simple evolutionary algorithm for the multi-objective minimum spanning tree problem , 2004, Eur. J. Oper. Res..
[6] Frank Neumann,et al. Heuristic strategies for solving complex interacting stockpile blending problem with chance constraints , 2021, GECCO.
[7] Frank Neumann,et al. Pareto Optimization for Subset Selection with Dynamic Cost Constraints , 2018, AAAI.
[8] Yang Yu,et al. On Subset Selection with General Cost Constraints , 2017, IJCAI.
[9] Frank Neumann,et al. Runtime analysis of RLS and the (1+1) EA for the chance-constrained knapsack problem with correlated uniform weights , 2021, GECCO.
[10] Yang Yu,et al. Subset Selection by Pareto Optimization , 2015, NIPS.
[11] Hiroaki Ishii,et al. Stochastic spanning tree problem , 1981, Discret. Appl. Math..
[12] Benjamin Doerr,et al. Multiplicative Drift Analysis , 2010, GECCO '10.
[13] Frank Neumann,et al. Optimising Monotone Chance-Constrained Submodular Functions Using Evolutionary Multi-Objective Algorithms , 2020, PPSN.
[14] Andrew M. Sutton,et al. Runtime analysis of the (1 + 1) evolutionary algorithm for the chance-constrained knapsack problem , 2019, FOGA '19.
[15] Benjamin Doerr,et al. Probabilistic Tools for the Analysis of Randomized Optimization Heuristics , 2018, Theory of Evolutionary Computation.
[16] Carsten Witt,et al. Approximating Covering Problems by Randomized Search Heuristics Using Multi-Objective Models , 2007, Evolutionary Computation.
[17] Ingo Wegener,et al. Theoretical Aspects of Evolutionary Algorithms , 2001, ICALP.
[18] Frank Neumann,et al. Evolutionary Bi-objective Optimization for the Dynamic Chance-Constrained Knapsack Problem Based on Tail Bound Objectives , 2020, ECAI.
[19] Thomas Bäck,et al. Theory of Evolutionary Computation: Recent Developments in Discrete Optimization , 2020, Theory of Evolutionary Computation.
[20] Yang Yu,et al. Evolutionary Learning: Advances in Theories and Algorithms , 2019 .
[21] Benjamin Doerr,et al. The Impact of Random Initialization on the Runtime of Randomized Search Heuristics , 2015, Algorithmica.
[22] Qingfu Zhang,et al. An Efficient Evolutionary Algorithm for Chance-Constrained Bi-Objective Stochastic Optimization , 2013, IEEE Transactions on Evolutionary Computation.
[23] Carsten Witt,et al. Tight Bounds on the Optimization Time of a Randomized Search Heuristic on Linear Functions† , 2013, Combinatorics, Probability and Computing.
[24] Frank Neumann,et al. Evolutionary algorithms for the chance-constrained knapsack problem , 2019, GECCO.
[25] Russ Bubley,et al. Randomized algorithms , 1995, CSUR.
[26] Frank Neumann,et al. Bioinspired computation in combinatorial optimization: algorithms and their computational complexity , 2010, GECCO '12.
[27] Frank Neumann,et al. Specific single- and multi-objective evolutionary algorithms for the chance-constrained knapsack problem , 2020, GECCO.
[28] Y. Z. Mehrjerdi. A Chance Constrained Programming , 2012 .
[29] Jafar Rezaei,et al. Convex hull ranking algorithm for multi-objective evolutionary algorithms , 2011, Sci. Iran..
[30] Oliver Giel,et al. Expected runtimes of a simple multi-objective evolutionary algorithm , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..
[31] Andrew M. Sutton,et al. Optimization of Chance-Constrained Submodular Functions , 2019, AAAI.
[32] Thomas Jansen,et al. Analyzing Evolutionary Algorithms: The Computer Science Perspective , 2012 .