计算机科学
符号回归
反向传播
遗传程序设计
渡线
进化计算
操作员(生物学)
人工智能
理论计算机科学
遗传算法
导线
搜索算法
遗传代表性
进化规划
机器学习
人工神经网络
算法
基因
转录因子
生物化学
抑制因子
化学
地理
大地测量学
作者
Tomasz P. Pawlak,Bartosz Wieloch,Krzysztof Krawiec
标识
DOI:10.1109/tevc.2014.2321259
摘要
In genetic programming, a search algorithm is expected to produce a program that achieves the desired final computation state (desired output). To reach that state, an executing program needs to traverse certain intermediate computation states. An evolutionary search process is expected to autonomously discover such states. This can be difficult for nontrivial tasks that require long programs to be solved. The semantic backpropagation algorithm proposed in this paper heuristically inverts the execution of evolving programs to determine the desired intermediate computation states. Two search operators, random desired operator and approximately geometric semantic crossover, use the intermediate states determined by semantic backpropagation to define subtasks of the original programming task, which are then solved using an exhaustive search. The operators outperform the standard genetic search operators and other semantic-aware operators when compared on a suite of symbolic regression and Boolean benchmarks. This result and additional analysis conducted in this paper indicate that semantic backpropagation helps evolution to identify the desired intermediate computation states and makes the search process more efficient.
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