论文标题
整数基因编程中的破坏传播失败
Failed Disruption Propagation in Integer Genetic Programming
论文作者
论文摘要
我们将随机的价值注入了对高度进化的深入整数GP树9743720次的评估中，并发现99.7％暗示交叉和突变的影响被消散，很少在程序外传播。实际上，对于递归的fibonacci gp树，在exp（-depth/3）和exp（-depth/5）之间的深度呈指数呈呈影响，而中断的误差则呈指数下降。信息理论解释了这一局部平坦的健身景观是由于FDP引起的。溢出并不重要，取而代之的是，整数GP，例如深符号回归浮点GP和一般的软件，并不脆弱，不健壮，不混乱，洛伦兹的蝴蝶几乎没有受苦。关键字：遗传算法，遗传编程，SBSE，信息丢失，信息漏斗，熵，可再生能力，突变性鲁棒性，最佳测试甲骨文放置，中性网络，软件鲁棒性，正确性吸引力，多样性，软件测试，膨胀理论，内含子，内含子，内含子，
We inject a random value into the evaluation of highly evolved deep integer GP trees 9743720 times and find 99.7percent Suggesting crossover and mutation's impact are dissipated and seldom propagate outside the program. Indeed only errors near the root node have impact and disruption falls exponentially with depth at between exp(-depth/3) and exp(-depth/5) for recursive Fibonacci GP trees, allowing five to seven levels of nesting between the runtime perturbation and an optimal test oracle for it to detect most errors. Information theory explains this locally flat fitness landscape is due to FDP. Overflow is not important and instead, integer GP, like deep symbolic regression floating point GP and software in general, is not fragile, is robust, is not chaotic and suffers little from Lorenz' butterfly. Keywords: genetic algorithms, genetic programming, SBSE, information loss, information funnels, entropy, evolvability, mutational robustness, optimal test oracle placement, neutral networks, software robustness, correctness attraction, diversity, software testing, theory of bloat, introns
