Reinforced Genetic Programming
Genetic Programming and Evolvable Machines
Development and the Baldwin effect
Artificial Life
Landscapes, learning costs, and genetic assimilation
Evolutionary Computation
The influence of learning on evolution: A mathematical framework
Artificial Life
A probabilistic memetic framework
IEEE Transactions on Evolutionary Computation
The baldwin effect in developing neural networks
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Comparison between lamarckian and baldwinian repair on multiobjective 0/1 knapsack problems
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
A study of the Lamarckian evolution of recurrent neural networks
IEEE Transactions on Evolutionary Computation
Systematic integration of parameterized local search into evolutionary algorithms
IEEE Transactions on Evolutionary Computation
A tutorial for competent memetic algorithms: model, taxonomy, and design issues
IEEE Transactions on Evolutionary Computation
Accelerating Differential Evolution Using an Adaptive Local Search
IEEE Transactions on Evolutionary Computation
Classification of adaptive memetic algorithms: a comparative study
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Baldwinian evolution is a type of hybridization of population-based global search and individual local search. The individuals take local refining processes, then in selection benefit from the improved fitness, but do not pass on the refined traits the data in to the offspring. The lost information of the refined phenotype implies that the inheritance encoded in genotypes is not directly benefit traits, but the traits having potential to achieve high fitness through the lifetime interaction with the environment. As the result, it is necessary to study how learning works comparing to the previous generation, in addition to how much it improves on the current population. The children may imitate what their parents performed and catch up with them, or alternatively, explore elsewhere and have no idea of where the parents arrived. In this paper, the trade-off is investigated, and it is revealed that in Baldwinian learning, the capability to follow the parents' footprints benefits. With higher imitation tendency, the evolving population can maintain a greater scale of learning potential, and the search results in better speed and convergence.