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Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2020 This book constitutes the refereed proceedings of the 23rd European Conference on Genetic Programming, EuroGP 2020, held as part of Evo*2020, in Seville, Spain, in April 2020, co-located with the Evo*2020 events EvoCOP, EvoMUSART and EvoApplications. The 12 full papers and 6 short papers presented in this book were carefully reviewed and selected from ...
Una-May O'Reilly, Erik Hemberg
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Inclusive Genetic Programming [PDF]
, 2021 The promotion and maintenance of the population diversity in a Genetic Programming (GP) algorithm was proved to be an important part of the evolutionary process. Such diversity maintenance improves the exploration capabilities of the GP algorithm, which as a consequence improves the quality of the found solutions by avoiding local optima.
Francesco Marchetti, Edmondo Minisci
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Expressive Genetic Programming [PDF]
Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion, 2016 The language in which evolving programs are expressed can have significant impacts on the dynamics and problem-solving capabilities of a genetic programming system. In GP these impacts are driven by far more than the absolute computational power of the languages used; just because a computation is theoretically possible in a language, it doesn't mean ...
Lee Spector, Nicholas Freitag McPhee
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Imperative Genetic Programming
SymmetryGenetic programming (GP) has a long-standing tradition in the evolution of computer programs, predominantly utilizing tree and linear paradigms, each with distinct advantages and limitations. Despite the rapid growth of the GP field, there have been disproportionately few attempts to evolve ’real’ Turing-like imperative programs (as contrasted with ...
Iztok Fajfar +6 more
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, 2005 Genetic programming (GP) is a sub-area of evolutionary computation first explored by John Koza (1992) and independently developed by Nichael Lynn Cramer (1985). It is a method for producing computer programs through adaptation according to a user-defined fitness criterion, or objective function. Like genetic algorithms, GP uses a representation related
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Revisiting Classical Controller Design and Tuning with Genetic Programming. [PDF]
Sensors (Basel), 2023 García CA +4 more
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A Genetic Programming Approach to Engineering MRI Reporter Genes. [PDF]
ACS Synth Biol, 2023 Bricco AR +8 more
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A genetic programming-based optimal sensor placement for greenhouse monitoring and control. [PDF]
Front Plant Sci, 2023 Ajani OS +5 more
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Creation of Recombinant Biocontrol Agents by Genetic Programming of Yeast. [PDF]
Acta Naturae, 2023 Pipiya SO +8 more
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