Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Local Search in Combinatorial Optimization
Local Search in Combinatorial Optimization
Tabu Search
Proceedings of the 5th International Conference on Genetic Algorithms
Genetic Local Search Algorithms for the Travelling Salesman Problem
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
Stochastic Hillclimbing as a Baseline Method for
Stochastic Hillclimbing as a Baseline Method for
Stochastic Local Search: Foundations & Applications
Stochastic Local Search: Foundations & Applications
Towards a New Evolutionary Computation: Advances on Estimation of Distribution Algorithms (Studies in Fuzziness and Soft Computing)
Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications (Studies in Computational Intelligence)
Scalability problems of simple genetic algorithms
Evolutionary Computation
Effects of a deterministic hill climber on hBOA
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Loopy Substructural Local Search for the Bayesian Optimization Algorithm
SLS '09 Proceedings of the Second International Workshop on Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics
Spurious dependencies and EDA scalability
Proceedings of the 12th annual conference on Genetic and evolutionary computation
The linkage tree genetic algorithm
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Optimal mixing evolutionary algorithms
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Substructural neighborhoods for local search in the bayesian optimization algorithm
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Linkage tree genetic algorithms: variants and analysis
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Learning the neighborhood with the linkage tree genetic algorithm
LION'12 Proceedings of the 6th international conference on Learning and Intelligent Optimization
Effects of discrete hill climbing on model building forestimation of distribution algorithms
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Hierarchical problem solving with the linkage tree genetic algorithm
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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The inclusion of local search (LS) techniques in evolutionary algorithms (EAs) is known to be very important in order to obtain competitive results on combinatorial and real-world optimization problems. Often however, an important source of the added value of LS is an understanding of the problem that allows performing a partial evaluation to compute the change in quality after only small changes were made to a solution. This is not possible in a Black-Box Optimization (BBO) setting. Here we take a closer look at the added value of LS when combined with EAs in a BBO setting. Moreover, we consider the interplay with model building, a technique commonly used in Estimation-of-Distribution Algorithms (EDAs) in order to increase robustness by statistically detecting and exploiting regularities in the optimization problem. We find, using two standardized hard BBO problems from EA literature, that LS can play an important role, especially in the interplay with model building in the form of what has become known as substructural LS. However, we also find that optimal mixing (OM), which indicates that operations in a variation operator are directly checked whether they lead to an improvement, is a superior combination of LS and EA.