This paper discusses the use of new graph structural genetic programming for automatic programming, which creates finite state machines (FSM) by evolution. Generally, FSM must define their transition rules for all combinations of states and possible inputs, thus the FSM program will become large and complex when the number of states and inputs is large. In our work, the nodes are connected by trajectory information sets, so it is possible that only the essential problem’s behavior obtained in the current situation are used in the network flow, and it can determine an action by not only the current, but also the past information. In addition, the proposed algorithm enhances evolutionary process by using fitness inheritance technique. Constraining the depth of genetic programming tree is one of the ways to overcome its bloat problem. Finally, fitness inherent is used when fitness evaluation is computationally expensive. Fitness inherent is based on averaging; therefore it reflects some assumptions of smoothness in the search space
목차
Abstract 1. Introduction 2. FSM Definition 1. Input-Output Specification (IOS): 2. Syntax Term (S) 3. Primitive Function (F) 4. Learning Parameter (a1 ) 5. Compl exity Paramet er (Tmax , β) 6. System Proof Plan (υ ) 3. Genetic Process Execution 3.1. Architecture Altering Operations: 4. Role of Data Trajectory Sets: 5. Fitness Inheritance 6. Conclusions References
보안공학연구지원센터(IJSEIA) [Science & Engineering Research Support Center, Republic of Korea(IJSEIA)]
설립연도
2006
분야
공학>컴퓨터학
소개
1. 보안공학에 대한 각종 조사 및 연구
2. 보안공학에 대한 응용기술 연구 및 발표
3. 보안공학에 관한 각종 학술 발표회 및 전시회 개최
4. 보안공학 기술의 상호 협조 및 정보교환
5. 보안공학에 관한 표준화 사업 및 규격의 제정
6. 보안공학에 관한 산학연 협동의 증진
7. 국제적 학술 교류 및 기술 협력
8. 보안공학에 관한 논문지 발간
9. 기타 본 회 목적 달성에 필요한 사업
간행물
간행물명
International Journal of Software Engineering and Its Applications
간기
월간
pISSN
1738-9984
수록기간
2008~2016
등재여부
SCOPUS
십진분류
KDC 505DDC 605
이 권호 내 다른 논문 / International Journal of Software Engineering and Its Applications Vol.3 No.4