Statistical tools for simulation practitioners
Statistical tools for simulation practitioners
Adaptive global optimization with local search
Adaptive global optimization with local search
Fitness inheritance in genetic algorithms
SAC '95 Proceedings of the 1995 ACM symposium on Applied computing
A Radial Basis Function Method for Global Optimization
Journal of Global Optimization
Sizing Populations for Serial and Parallel Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Adaptive dimension reduction for clustering high dimensional data
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
A comprehensive survey of fitness approximation in evolutionary computation
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Constrained Global Optimization of Expensive Black Box Functions Using Radial Basis Functions
Journal of Global Optimization
New methods for competitive coevolution
Evolutionary Computation
A review of recent advances in global optimization
Journal of Global Optimization
A genetic algorithm that adaptively mutates and never revisits
IEEE Transactions on Evolutionary Computation
Chromosome reuse in genetic algorithms
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Mechanisms for evolutionary reincarnation
ACAL'07 Proceedings of the 3rd Australian conference on Progress in artificial life
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Hybridization of evolutionary algorithms and local search by means of a clustering method
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Survey of clustering algorithms
IEEE Transactions on Neural Networks
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We examine the concept of storing all evaluated chromosomes and directly reuse them in Genetic Algorithms (GAs). This is achieved by a fully encapsulated operator, called Registrar, which is effortlessly placed between the GA and the objective function. The Registrar does not approximate the objective function. Instead, it replaces the chromosomes requested by the GA with similar ones taken from the registry, bypassing the function evaluation. Unlike other methods that use external memory to increase genetic diversity, our simple implementation encourages revisits in order to avoid evaluations in an aggressive manner. Significant increase in performance is observed which is present even at the early stages of evolution, in accordance with the Birthday Problem of probability theory. Implementation with Standard GA shows great promise, while the encapsulation of the code facilitates implementation with other Evolutionary Algorithms.