What Makes a Problem Hard for a Genetic Algorithm? Some Anomalous Results and Their Explanation
Machine Learning - Special issue on genetic algorithms
An introduction to genetic algorithms
An introduction to genetic algorithms
Embedding Gestalt Laws in Markov Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cross-entropy and rare events for maximal cut and partition problems
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue: Rare event simulation
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
A Survey of Optimization by Building and Using Probabilistic Models
Computational Optimization and Applications
Modeling Building-Block Interdependency
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Repair and Brood Selection in the Traveling Salesman Problem
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
A Racing Algorithm for Configuring Metaheuristics
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
A Hyperheuristic Approach to Scheduling a Sales Summit
PATAT '00 Selected papers from the Third International Conference on Practice and Theory of Automated Timetabling III
Dynamic Representations and Escaping Local Optima: Improving Genetic Algorithms and Local Search
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Some NP-complete geometric problems
STOC '76 Proceedings of the eighth annual ACM symposium on Theory of computing
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
Causal architecture, complexity and self-organization in time series and cellular automata
Causal architecture, complexity and self-organization in time series and cellular automata
Compositional evolution: interdisciplinary investigations in evolvability, modularity, and symbiosis
Compositional evolution: interdisciplinary investigations in evolvability, modularity, and symbiosis
Stochastic Local Search: Foundations & Applications
Stochastic Local Search: Foundations & Applications
An analysis of cooperative coevolutionary algorithms
An analysis of cooperative coevolutionary algorithms
Compositional Evolution: The Impact of Sex, Symbiosis, and Modularity on the Gradualist Framework of Evolution (Vienna Series in Theoretical Biology)
Representations for Genetic and Evolutionary Algorithms
Representations for Genetic and Evolutionary Algorithms
A gestalt genetic algorithm: less details for better search
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A new representation and operators for genetic algorithms applied to grouping problems
Evolutionary Computation
An analysis of island models in evolutionary computation
An analysis of island models in evolutionary computation
Reactive Search and Intelligent Optimization
Reactive Search and Intelligent Optimization
CoEvolution of effective observers and observed multi-agents system
ECAL'05 Proceedings of the 8th European conference on Advances in Artificial Life
IEEE Transactions on Evolutionary Computation
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Nowadays, many engineering applications require the minimization of a cost function such as decreasing the delivery time or the used space, reducing the development effort, and so on. Not surprisingly, research in optimization is one of the most active fields of computer science. Metaheuristics are part of the state-of-the-art techniques for combinatorial optimization. But their success comes at the price of considerable efforts in design and development time. Can we go further and automate their preparation? Especially when time is limited, dedicated techniques are unknown or the tackled problem is not well understood? The Gestalt heuristic, a search based on meta-modeling, answers those questions. Our approach, inspired by Gestalt psychology, considers the problem representation as a key factor of the success of the metaheuristic search process. Thanks to the emergence of such representation abstraction, the metaheuristic is being assisted by constraining the search. This abstraction is mainly based on the aggregation of the representation variables. The metaheuristic operators then work with these new aggregates. By learning, the Gestalt heuristic continuously searches for the right level of abstraction. It turns out to be an engineering mechanism very much related with the intrinsic emergence concept. First, the paper introduces the approach in the practical context of combinatorial optimization. It describes one possible implementation with evolutionary algorithms. Then, several experimental studies and results are presented and discussed in order to test the suggested Gestalt heuristic implementation and its main characteristics. Finally, the heuristic is more conceptually discussed in the context of emergence.