636 results on '"Marc Schoenauer"'
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52. Greedy Semantic Local Search for Small Solutions.
53. Parameter Setting for Multicore CMA-ES with Large Populations.
54. Demand-Side Management: Optimising Through Differential Evolution Plug-in Electric Vehicles to Partially Fulfil Load Demand.
55. Design of an Autonomous Intelligent Demand-Side Management System by using Electric Vehicles as Mobile Energy Storage Units by Means of Evolutionary Algorithms.
56. True Pareto Fronts for Multi-objective AI Planning Instances.
57. Memetic Semantic Genetic Programming.
58. Memetic Semantic Genetic Programming for Symbolic Regression
59. Maximum Likelihood-Based Online Adaptation of Hyper-Parameters in CMA-ES.
60. Racing Multi-objective Selection Probabilities.
61. Programming by Feedback.
62. The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities.
63. An analogy based approach for solving target sense verification
64. Multi-objective AI Planning: Evaluating DaE YAHSP on a Tunable Benchmark.
65. Quality Measures of Parameter Tuning for Aggregated Multi-Objective Temporal Planning.
66. Hybridizing Constraint Programming and Monte-Carlo Tree Search: Application to the Job Shop Problem.
67. Pareto-Based Multiobjective AI Planning.
68. Multiobjective tactical planning under uncertainty for air traffic flow and capacity management.
69. Multi-objective AI Planning: Comparing Aggregation and Pareto Approaches.
70. Intensive surrogate model exploitation in self-adaptive surrogate-assisted cma-es (saacm-es).
71. Bi-population CMA-ES agorithms with surrogate models and line searches.
72. Sustainable cooperative coevolution with a multi-armed bandit.
73. Bandit-Based Search for Constraint Programming.
74. A Multi-objective Approach to Balance Buildings Construction Cost and Energy Efficiency.
75. Alternative Restart Strategies for CMA-ES.
76. Pilot, Rollout and Monte Carlo Tree Search Methods for Job Shop Scheduling.
77. APRIL: Active Preference Learning-Based Reinforcement Learning.
78. Black-box optimization benchmarking of IPOP-saACM-ES and BIPOP-saACM-ES on the BBOB-2012 noiseless testbed.
79. Black-box optimization benchmarking of NIPOP-aCMA-ES and NBIPOP-aCMA-ES on the BBOB-2012 noiseless testbed.
80. Black-box optimization benchmarking of IPOP-saACM-ES on the BBOB-2012 noisy testbed.
81. Asynchronous master/slave moeas and heterogeneous evaluation costs.
82. Self-adaptive surrogate-assisted covariance matrix adaptation evolution strategy.
83. Dynamic GP fitness cases in static and dynamic optimisation problems.
84. Not All Parents Are Equal for MO-CMA-ES.
85. On-Board Evolutionary Algorithm and Off-Line Rule Discovery for Column Formation in Swarm Robotics.
86. Artificial gene regulatory networks and spatial computation: A case study.
87. Learn-and-Optimize: A Parameter Tuning Framework for Evolutionary AI Planning.
88. A Rigorous Runtime Analysis for Quasi-Random Restarts and Decreasing Stepsize.
89. Asynchronous Evolutionary Multi-Objective Algorithms with heterogeneous evaluation costs.
90. Preference-Based Policy Learning.
91. Instance-based parameter tuning for evolutionary AI planning.
92. Optimizing architectural and structural aspects of buildings towards higher energy efficiency.
93. Adaptive coordinate descent.
94. Cellular Automata with Irregular Structure: A Compact Representation.
95. Evolving Genes to Balance a Pole.
96. Comparison-Based Optimizers Need Comparison-Based Surrogates.
97. Comparison-Based Adaptive Strategy Selection with Bandits in Differential Evolution.
98. Open-Ended Evolutionary Robotics: An Information Theoretic Approach.
99. An Evolutionary Metaheuristic Based on State Decomposition for Domain-Independent Satisficing Planning.
100. On the Benefit of Sub-optimality within the Divide-and-Evolve Scheme.
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