Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
The Simple Genetic Algorithm: Foundations and Theory
The Simple Genetic Algorithm: Foundations and Theory
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Lamarckian Evolution, The Baldwin Effect and Function Optimization
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
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Vose's dynamical systems model of the simple genetic algorithm (SGA) is an exact model that uses mathematical operations to capture the dynamical behavior of genetic algorithms. The original model was defined for a simple genetic algorithm. This paper suggests how to extend the model and incorporate two kinds of learning, Darwinian and Lamarckian, into the framework of the Vose model. The extension provides a new theoretical framework to examine the effects of lifetime learning on the fitness of a population. We analyze the asymptotic behavior of different hybrid algorithms on an infinite population vector and compare it to the behavior of the classical genetic algorithm on various population sizes. Our experiments show that Lamarckian-like inheritance - direct transfer of lifetime learning results to offsprings - allows quicker genetic adaptation. However, functions exist where the simple genetic algorithms without learning, as well as Lamarckian evolution, converge to the same local optimum, while genetic search based on Darwinian inheritance converges to the global optimum.