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GECCO
1999
Springer

Coevolutionary Genetic Algorithms for Solving Dynamic Constraint Satisfaction Problems

10 years 2 months ago
Coevolutionary Genetic Algorithms for Solving Dynamic Constraint Satisfaction Problems
In this paper, we discuss the adaptability of Coevolutionary Genetic Algorithms on dynamic environments. Our CGA consists of two populations: solution-level one and schema-level one. The solution-level population searches for the good solution in a given problem. The schema-level population searches for the good schemata in the former population. Our CGA performs effectively by exchanging genetic information between these populations. Also, we define Dynamic Constraint Satisfaction Problems as such dynamic environments. General CSPs are defined by two stochastic parameters: density and tightness, then, Dynamic CSPs are defined as a sequence of static constraint networks of General CSPs. Computational results on DCSPs confirm us the effectiveness of our approach.
Hisashi Handa, Osamu Katai, Tadataka Konishi, Mits
Added 04 Aug 2010
Updated 04 Aug 2010
Type Conference
Year 1999
Where GECCO
Authors Hisashi Handa, Osamu Katai, Tadataka Konishi, Mitsuru Baba
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