Efficient and Accurate Parallel Genetic Algorithms
Efficient and Accurate Parallel Genetic Algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Future Generation Computer Systems - Special issue on bio-impaired solutions to parallel processing problems
Dynamic CSPs for Interval-Based Temporal Reasoning
IEA/AIE '02 Proceedings of the 15th international conference on Industrial and engineering applications of artificial intelligence and expert systems: developments in applied artificial intelligence
Adaptive Penalties for Evolutionary Graph Coloring
AE '97 Selected Papers from the Third European Conference on Artificial Evolution
Constraint Processing
Future Generation Computer Systems - Special issue: Geocomputation
AI Communications - Special issue on: Spatial and temporal reasoning
Efficient Hierarchical Parallel Genetic Algorithms using Grid computing
Future Generation Computer Systems
Solving Temporal Constraint Satisfaction Problems with Heuristic Based Evolutionary Algorithms
ICTAI '08 Proceedings of the 2008 20th IEEE International Conference on Tools with Artificial Intelligence - Volume 02
Exact phase transitions in random constraint satisfaction problems
Journal of Artificial Intelligence Research
Heuristic techniques for variable and value ordering in CSPs
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Managing dynamic CSPs with preferences
Applied Intelligence
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In this paper we investigate the applicability of Genetic Algorithms (GAs) for solving Constraint Satisfaction Problems (CSPs). Despite some success of GAs when tackling CSPs, they generally suffer from poor crossover operators. In order to overcome this limitation in practice, we propose a novel crossover specifically designed for solving CSPs. Together with a variable ordering heuristic and an integration into a parallel architecture, this proposed crossover enables the solving of large and hard problem instances as demonstrated by the experimental tests conducted on randomly generated CSPs based on the model RB. We will indeed demonstrate, through these tests, that our proposed method is superior to the known GA based techniques for CSPs. In addition, we will show that we are able to compete with the efficient MAC-based Abscon 109 solver for random problem instances.