An Examination of Lamarckian Genetic Algorithms
نویسندگان
چکیده
In keeping with the spirit of Lamarckian evolution, variations on a simple genetic algorithm are compared, in which each individual is optimized prior to evaluation. Four different optimization techniques in all are tested: random hillclimbing, social (memetic) exchange, and two techniques using artificial neural nets (ANNs). These techniques are tested on a set of three sample problems: an instance of a minimum-spanning tree problem, an instance of a travelling salesman problem, and a problem where ANNs are evolved to generate a random sequence of bits. The results suggest that in general, social exchange provides the best performance, consistently outperforming the non-optimized genetic algorithm; results for other optimization techniques are less compelling.
منابع مشابه
A Lamarckian Evolution Strategy for Genetic Algorithms
A Prolog implementation of a simple Lamarckian evolution module for genetic algorithms is discussed. Lamarckian evolution posits that characteristics acquired during a phenotype's lifetime may be inherited by offspring. Although largely dismissed as a viable evolutionary theory for natural systems, Lamarckian evolution has proven effective within computer applications. The strengths of the impl...
متن کاملLamarckian Evolution , The Baldwin E ectand Function
We compare two forms of hybrid genetic search. The rst uses Lamarckian evolution, while the second uses a related method where local search is employed to change the tness of strings, but the acquired improvements do not change the genetic encoding of the individual. The latter search method exploits the Baldwin eeect. By modeling a simple genetic algorithm we show that functions exist where si...
متن کاملAdding Learning to the Cellular Development of Neural Networks: Evolution and the Baldwin Effect
A grammar tree is used to encode a cellular developmental process that can generate whole families of Boolean neural networks for computing parity and symmetry. The development process resembles biological cell division. A genetic algorithm is used to nd a grammar tree that yields both architecture and weights specifying a particular neural network for solving speciic Boolean functions. The cur...
متن کاملLamarckian Evolution and the Baldwin Effect in Evolutionary Neural Networks
Resumen— Hybrid neuro-evolutionary algorithms may be inspired on Darwinian or Lamarckian evolution. In the case of Darwinian evolution, the Baldwin effect, that is, the progressive incorporation of learned characteristics to the genotypes, can be observed and leveraged to improve the search. The purpose of this paper is to carry out an experimental study into how learning can improve G-Prop gen...
متن کاملA Lamarckian Model Combining Levenberg-marquardt Algorithm and a Genetic Algorithm
We review the integration between the genetic and evolutionary techniques with artificial neural networks. A Lamarckian model is proposed based on genetic algorithms and artificial neural networks. The genetic algorithm evolves the population while the artificial neural network performs the learning process. The direct encoding scheme was used. This model was submitted to several data sets and ...
متن کامل