By Hidefumi Sawai (auth.), Hidefumi Sawai (eds.)
By incorporating biologically-inspired capabilities into ICT, quite a few varieties of new-generation details and verbal exchange platforms should be created. just a few instance of parts already taking advantage of such layout idea are community architectures, details processing, molecular conversation, and complicated community modeling for fixing actual world-problems. This publication offers the theoretical foundation for knowing those advancements and explains their sensible functions. Highlighted inserts seems all through to assist readers to appreciate the very most up-to-date themes in those rising study fields. The e-book ends with a extra philosophical dialogue on how new ICT ideas are available through taking a look at analogous platforms in biology. This new frame of mind may also help researchers and practitioners to use cutting edge rules in constructing next-generation technologies.
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Additional resources for Biological Functions for Information and Communication Technologies: Theory and Inspiration
A real-valued genetic algorithm (real-valued GA) is used as the evolutionary computational model. The methods of genetic recombination are chromosomal exchange between individuals and isotropic mutation of each gene. Sexual selection will focus on relative phenotypic value. ) In such modeling schemes, the direction of the transition of next-generation males in phenotypic space is determined by the direction of female preferences. 2 Individual Phenotype Each individual is assigned a sex and with two broad phenotypic categories, trait and preference.
If not, GP repeats the series of genetic manipulations above while advancing from generation to generation. Genetic programming is widely applied as an approach to solving realworld problems in various areas of artificial intelligence, including symbolic regression problems, system identification, optimization control, planning, time series predictions, automatic programming, discovery of game strategies, solutions for inverse problems, rules discovery, pattern classification, evolution of emergent behavior, automatic programming of cellular automata, and evolvable hardware (EHW).
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