Titlebook: Genetic Programming Theory and Practice V; Rick Riolo,Terence Soule,Bill Worzel Book 2008 Springer-Verlag US 2008 Algorithms.Multi-agent s

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Towards an Information Theoretic Framework for Genetic Programming,n proceeds, this approach suggests that the mutual information between the target and models should: (i) not decrease in the population; (ii) concentrate in fewer individuals; and (iii) be “distilled” from the inputs, eliminating excess entropy. Normalized information theoretic indices are developed

Yag-Capsulotomy

Investigating Problem Hardness of Real Life Applications,algebraic difficulty indicator. The indicator used is the Negative Slope Coefficient, whose efficacy has been recently empirically demonstrated on a large set of hand-tailored theoretical test functions and well known GP benchmarks. The real-life problems studied belong to the field of Biomedical ap

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Programstructure-Fitnessdisconnect and Its Impact on Evolution in Genetic Programming,ny cases, the genotype and the phenotype are considered identical in GP since the program representation does not undergo any modification prior to its encounter with “environment” in the form of inputs and a fitness function. However, this view overlooks a key fact: fitness in GP is determined with

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Genetic Programmingwith Reuse of Known Designs for Industrially Scalable, Novel Circuit Design,issues for synthesized designs, for genetic programming applied to automated structural design. Furthermore, it uses trustworthiness tradeoffs to handle addition of novelty in a trackable fashion. It uses a multi-objective algorithm with an age-layered population structure to avoid premature converg