| 期刊全稱(chēng) | Behavioral Program Synthesis with Genetic Programming | | 影響因子2023 | Krzysztof Krawiec | | 視頻video | http://file.papertrans.cn/183/182884/182884.mp4 | | 發(fā)行地址 | Recent research in Behavioral Program Synthesis with Genetic Programming.Presents application of genetic programming.Written by an expert in the field.Includes supplementary material: | | 學(xué)科分類(lèi) | Studies in Computational Intelligence | | 圖書(shū)封面 |  | | 影響因子 | .Genetic programming (GP) is a popular heuristic methodology of program synthesis with origins in evolutionary computation. In this generate-and-test approach, candidate programs are iteratively produced and evaluated. The latter involves running programs on tests, where they exhibit complex behaviors reflected in changes of variables, registers, or memory. That behavior not only ultimately determines program output, but may also reveal its `hidden qualities‘ and important characteristics of the considered synthesis problem. However, the conventional GP is oblivious to most of that information and usually cares only about the number of tests passed by a program. This `evaluation bottleneck‘ leaves search algorithm underinformed about the actual and potential qualities of candidate programs..?.This book proposes behavioral program synthesis, a conceptual framework that opens GP to detailed information on program behavior in order to make program synthesis more efficient.Several existing and novel mechanisms subscribing to that perspective to varying extent are presented and discussed, including implicit fitness sharing, semantic GP, co-solvability, trace convergence analysis, patter | | Pindex | Book 2016 |
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