面向语义的笛卡尔遗传编程变异算子在电路进化设计中的应用
Semantically-Oriented Mutation Operator in Cartesian Genetic Programming for Evolutionary Circuit Design
摘要：
尽管有许多成功的应用，笛卡尔遗传编程（CGP）遭受有限的可扩展性，特别是当用于进化电路设计。例如，考虑到乘法器设计问题，5x5位乘法器代表了从随机生成的初始种群演化而来的最复杂的电路。CGP的效率在很大程度上取决于点变异算子的性能，然而，该算子是纯随机的。这与遗传编程（GP）的最新发展形成鲜明对比，其中引入了高级知情方法，如语义感知算子，以提高GP的搜索空间探索能力。本文提出了一种适用于组合电路进化设计的面向语义的变异算子（SOMO）。SOMO使用语义来确定每个突变基因的最佳值。与常见的CGP及其变体以及最近版本的语义GP相比，所提出的方法在保持表型大小相对较小的同时，在常见的布尔基准上收敛得更快。本文给出的成功进化实例包括10位奇偶校验、10+10位加法器和5 × 5位乘法器。最复杂的电路在不到一个小时的时间内就完成了，并且在一个普通的CPU上运行了一个单线程实现。
英文摘要：
Despite many successful applications, Cartesian Genetic Programming (CGP) suffers from limited scalability, especially when used for evolutionary circuit design. Considering the multiplier design problem, for example, the 5x5-bit multiplier represents the most complex circuit evolved from a randomly generated initial population. The efficiency of CGP highly depends on the performance of the point mutation operator, however, this operator is purely stochastic. This contrasts with the recent developments in Genetic Programming (GP), where advanced informed approaches such as semantic-aware operators are incorporated to improve the search space exploration capability of GP. In this paper, we propose a semantically-oriented mutation operator (SOMO) suitable for the evolutionary design of combinational circuits. SOMO uses semantics to determine the best value for each mutated gene. Compared to the common CGP and its variants as well as the recent versions of Semantic GP, the proposed method converges on common Boolean benchmarks substantially faster while keeping the phenotype size relatively small. The successfully evolved instances presented in this paper include 10-bit parity, 10+10-bit adder and 5x5-bit multiplier. The most complex circuits were evolved in less than one hour with a single-thread implementation running on a common CPU.
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