Solving randomly generated constraint satisfaction problems using a micro-evolutionary hybrid that evolves a population of hill-climbers

This paper introduces a new micro-evolutionary search technique which combines the concept of evolutionary searching with the systematic search concept of hill climbing to form a hybrid that quickly find solutions to constraint satisfaction problems. This new hybrid outperforms a well-known hill climber, the iterative descent method (IDM), on a test suite of 750 randomly-generated constraint satisfaction problems.