Relational inductive learning with a hybrid evolutionary algorithm

作     者：Divina, F 

作者机构：Free Univ Amsterdam Dept Comp Sci NL-1081 HV Amsterdam Netherlands 

出 版 物：《AI COMMUNICATIONS》 (人工智能通讯)

年 卷 期：2005年第18卷第1期

页      面：67-69页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：supervised learning Evolutionary Algorithms Inductive Logic Programming 

摘      要：Inductive learning in First-Order Logic (FOL) is a hard task due to both the prohibitive size of the search space and the computational cost of evaluating hypotheses. This paper describes an evolutionary algorithm for concept learning in (a fragment of) FOL. The algorithm, called ECL (for Evolutionary Concept Learner), evolves a population of Horn clauses by repeated selection, mutation and optimization of more fit clauses. ECL relies on four greedy mutation operators for searching the hypothesis space, and employs an optimization phase that follows each mutation. Experimental results show that ECL works well in practice.