Benchmarking a (μ +λ ) Genetic Algorithm with Configurable Crossover Probability
暂无分享,去创建一个
Thomas Bäck | Carola Doerr | Furong Ye | Hao Wang | Thomas Bäck | Carola Doerr | Furong Ye | Hao Wang
[1] Ingo Wegener,et al. Real royal road functions--where crossover provably is essential , 2001, Discret. Appl. Math..
[2] Dogan Corus,et al. On the Benefits of Populations for the Exploitation Speed of Standard Steady-State Genetic Algorithms , 2020, Algorithmica.
[3] Thomas Jansen,et al. A building-block royal road where crossover is provably essential , 2007, GECCO '07.
[4] Ruhul A. Sarker,et al. Multi-operator based evolutionary algorithms for solving constrained optimization problems , 2011, Comput. Oper. Res..
[5] Per Kristian Lehre,et al. Crossover can be constructive when computing unique input–output sequences , 2011, Soft Comput..
[6] Thomas Bäck,et al. Evolutionary algorithms in theory and practice - evolution strategies, evolutionary programming, genetic algorithms , 1996 .
[7] Benjamin Doerr,et al. From black-box complexity to designing new genetic algorithms , 2015, Theor. Comput. Sci..
[8] Per Kristian Lehre,et al. Black-Box Search by Unbiased Variation , 2010, GECCO '10.
[9] C. D. Gelatt,et al. Optimization by Simulated Annealing , 1983, Science.
[10] Kenneth A. De Jong,et al. Design and Management of Complex Technical Processes and Systems by Means of Computational Intelligence Methods on the Choice of the Offspring Population Size in Evolutionary Algorithms on the Choice of the Offspring Population Size in Evolutionary Algorithms , 2004 .
[11] Tadahiko MURATA,et al. Positive and negative combination effects of crossover and mutation operators in sequencing problems , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.
[12] Maxim Buzdalov,et al. Evaluation of heavy-tailed mutation operator on maximum flow test generation problem , 2017, GECCO.
[13] William M. Spears,et al. Crossover or Mutation? , 1992, FOGA.
[14] John H. Holland,et al. When will a Genetic Algorithm Outperform Hill Climbing , 1993, NIPS.
[15] Benjamin Doerr,et al. Crossover can provably be useful in evolutionary computation , 2012, Theor. Comput. Sci..
[16] Hao Wang,et al. Towards a theory-guided benchmarking suite for discrete black-box optimization heuristics: profiling (1 + λ) EA variants on onemax and leadingones , 2018, GECCO.
[17] Dirk Sudholt,et al. How Crossover Speeds up Building Block Assembly in Genetic Algorithms , 2014, Evolutionary Computation.
[18] Anirban Mukhopadhyay,et al. Exploration and Exploitation Without Mutation: Solving the Jump Function in \varTheta (n) Time , 2018, PPSN.
[19] Kurt Mehlhorn,et al. The Query Complexity of Finding a Hidden Permutation , 2013, Space-Efficient Data Structures, Streams, and Algorithms.
[20] Thomas Bäck,et al. Benchmarking a $(\mu+\lambda)$ Genetic Algorithm with Configurable Crossover Probability , 2020 .
[21] Benjamin Doerr,et al. Black-Box Complexity: Breaking the O(n logn) Barrier of LeadingOnes , 2011, Artificial Evolution.
[22] L. Darrell Whitley,et al. The massively parallel mixing genetic algorithm for the traveling salesman problem , 2019, GECCO.
[23] Per Kristian Lehre,et al. Escaping Local Optima Using Crossover With Emergent Diversity , 2018, IEEE Transactions on Evolutionary Computation.
[24] Thomas Weise,et al. Difficult features of combinatorial optimization problems and the tunable w-model benchmark problem for simulating them , 2018, GECCO.
[25] Enrique Alba,et al. Fitness Probability Distribution of Bit-Flip Mutation , 2013, Evolutionary Computation.
[26] Carola Doerr,et al. Maximizing Drift Is Not Optimal for Solving OneMax , 2019, Evolutionary Computation.
[27] Frank Neumann,et al. More effective crossover operators for the all-pairs shortest path problem , 2013, Theor. Comput. Sci..
[28] Kenneth Alan De Jong,et al. An analysis of the behavior of a class of genetic adaptive systems. , 1975 .
[29] David E. Goldberg,et al. Genetic Algorithms in Search Optimization and Machine Learning , 1988 .
[30] Dirk Sudholt,et al. Crossover is provably essential for the Ising model on trees , 2005, GECCO '05.
[31] Benjamin Doerr,et al. Fast genetic algorithms , 2017, GECCO.
[32] Pietro Simone Oliveto,et al. On the effectiveness of crossover for migration in parallel evolutionary algorithms , 2011, GECCO '11.
[33] Frank Neumann,et al. Optimal Fixed and Adaptive Mutation Rates for the LeadingOnes Problem , 2010, PPSN.
[34] Ofer M. Shir,et al. Benchmarking discrete optimization heuristics with IOHprofiler , 2020, Appl. Soft Comput..
[35] Carola Doerr,et al. A Simple Proof for the Usefulness of Crossover in Black-Box Optimization , 2018, PPSN.
[36] Dirk Sudholt,et al. A New Method for Lower Bounds on the Running Time of Evolutionary Algorithms , 2011, IEEE Transactions on Evolutionary Computation.
[37] Dirk Sudholt,et al. How crossover helps in pseudo-boolean optimization , 2011, GECCO '11.
[38] Thomas Jansen,et al. The Analysis of Evolutionary Algorithms—A Proof That Crossover Really Can Help , 2002, Algorithmica.
[39] Byung Ro Moon,et al. An empirical study on the synergy of multiple crossover operators , 2002, IEEE Trans. Evol. Comput..
[40] Benjamin Doerr,et al. Analyzing randomized search heuristics via stochastic domination , 2019, Theor. Comput. Sci..
[41] Dogan Corus,et al. Standard Steady State Genetic Algorithms Can Hillclimb Faster Than Mutation-Only Evolutionary Algorithms , 2017, IEEE Transactions on Evolutionary Computation.
[42] Hector J. Levesque,et al. A New Method for Solving Hard Satisfiability Problems , 1992, AAAI.
[43] Hao Wang,et al. IOHprofiler: A Benchmarking and Profiling Tool for Iterative Optimization Heuristics , 2018, ArXiv.