Using reproductive evaluation to improve genetic search and heuristic discovery
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Toward a unified thermodynamic genetic operator
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Spin-flip symmetry and synchronization
Evolutionary Computation
Uniform Crossover in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
A Refined Genetic Algorithm for Parameter Optimization Problems
Proceedings of the 5th International Conference on Genetic Algorithms
The Symbiotic Evolution of Solutions and Their Representations
Proceedings of the 6th International Conference on Genetic Algorithms
Properties of symmetric fitness functions
Proceedings of the 8th annual conference on Genetic and evolutionary computation
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Classical Genetic Algorithm theory was built on four operators: proportional selection, one-point crossover, mutation and inversion. While the role of inversion was questioned, the use of the other remaining operators has thrived, some of these newly designed operators being motivated by good empirical results, some having a solid theory to support their use. In this paper we present a Simple Inversion Operator, and we investigate its potential mixing capabilities for problems where the optimum consists of juxtaposed Symmetric Building Blocks. Both theoretical investigation and experimental results obtained, indicate that our operator is quite powerful in finding the right building blocks that compose the optimum, whenever symmetrical building blocks play an important role in the discovery of the global solution.