Abstract
Three different levels of sophistication of genetic algoriithms (GAs) are described. (1) The first level are GAs, where the chromosome fitness is determined exactly by the chromosome position on the fitness landscape. This first level of sophistication corresponds to standard GAs, where chromosomes are directly mapped on positive real numbers. (2) In the second level the chromosome fitness is determined by the nearest neighborhood of the chromosome position on the fitness landscape, i.e. chromosomes are capable of learning. The role of learning in evolution theory called the Baldwin effect was first studied by GAs in 1987 by Hinton and Nowlan. (3) In the third level the chromosome fitness is determined not only by the nearest neighborhood of the chromosome position on the fitness landscape but also by the so-called meme that determines an information that is able to increase the fitness of chromosomes. The idea of memes was introduced to evolutionary biology by Dawkins. The concept of chromosome is enlarged to a complex of chromosome and meme, both of them determine a fitness of chromosomes itself. All the above three different levels of GAs may be formally considered as evolutionary steps of Darwin’s evolution. The purpose of this communication is to present a GA simulation of learning and Dawkins’ memes.
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© 1999 Springer-Verlag London Limited
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Kvasnička, V., Pospíchal, J. (1999). Simulation of Baldwin effect and Dawkins memes by genetic algorithm. In: Roy, R., Furuhashi, T., Chawdhry, P.K. (eds) Advances in Soft Computing. Springer, London. https://doi.org/10.1007/978-1-4471-0819-1_36
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DOI: https://doi.org/10.1007/978-1-4471-0819-1_36
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