Evolutionary computation: toward a new philosophy of machine intelligence
Evolutionary computation: toward a new philosophy of machine intelligence
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
A Comparison of Parallel and Sequential Niching Methods
Proceedings of the 6th International Conference on Genetic Algorithms
Using Decision Tree Induction for Discovering Holes in Data
PRICAI '98 Proceedings of the 5th Pacific Rim International Conference on Artificial Intelligence: Topics in Artificial Intelligence
Discovering interesting holes in data
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Real-parameter genetic algorithms for finding multiple optimal solutions in multi-modal optimization
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
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In Genetic algorithms it is not easy to evaluate the confidence level in whether a GA run may have missed a complete area of good points, and whether the global optimum was found. We accept this but hope to add some degree of confidence in our results by showing that no large gaps were left unvisited in the search space. This can be achieved to some extent by inserting new individuals in big empty spaces. However it is not easy to find the biggest empty spaces, particularly in multi-dimensional problems. For a GA problem, however, it is not necessary to find the exact biggest empty spaces; a sufficiently large empty space is good enough to insert new individuals. In this paper, we present a method to find a sufficiently large empty Hyper-Rectangle for new individual insertion in a GA while keeping the computational complexity as a polynomial function. Its merit is demonstrated in several domains.