Combinatorial optimization: algorithms and complexity
Combinatorial optimization: algorithms and complexity
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Genetic Algorithm and Graph Partitioning
IEEE Transactions on Computers
Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
Isomorphism, Normalization, And A Genetic Algorithm For Sorting Network Optimization
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Normalization in genetic algorithms
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Problem-independent schema synthesis for genetic algorithms
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Investigation of the fitness landscapes and multi-parent crossover for graph bipartitioning
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
New usage of Sammon's mapping for genetic visualization
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Fitness landscape analysis and memetic algorithms for the quadratic assignment problem
IEEE Transactions on Evolutionary Computation
Multi-attractor gene reordering for graph bisection
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Geometric crossover for multiway graph partitioning
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Representation and recombination over nonsingular binary matrices
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Quotient geometric crossovers and redundant encodings
Theoretical Computer Science
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We propose three distance measures for genetic search space. One is a distance measure in the population space that is useful for understanding the working mechanism of genetic algorithms. Another is a distance measure in the solution space for K-grouping problems. This can be used for normalization in crossover. The third is a level distance measure for genetic algorithms, which is useful for measuring problem difficulty with respect to genetic algorithms. We show that the proposed measures are metrics and the measures are efficiently computed.