Constraint satisfaction in logic programming
Constraint satisfaction in logic programming
Some Guidelines for Genetic Algorithms with Penalty Functions
Proceedings of the 3rd International Conference on Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Learning at the crossroads of biology and computation
INBS '95 Proceedings of the First International Symposium on Intelligence in Neural and Biological Systems (INBS'95)
Applying genetic algorithm for the development of the components-based embedded system
Computer Standards & Interfaces
Artificial Life
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
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This paper introduces GSSS (Genetic State-Space Search). The integration of two general search paradigms--genetic search and state-space-search - provides a general framework which can be applied to a large variety of search problems. Here, we show how GSSS solves constrained optimization problems (COPs). Basically, it searches for "promising search states" from which good solutions can be easily found. Domain knowledge in the form of constraints is used to limit the space to be searched. Interestingly, our approach allows the handling of constraints within genetic search at a general domain independent level. First, we introduce a genetic representation of search states. Next, we provide empirical results which compare the relative merit of the introduction of constraints during the generation of the initial population, during the fitness calculation, and during the application of genetic operators. Finally, we describe some extensions to our method which came about when applying it to factory floor scheduling problems.