• 제어로봇시스템학회논문지
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• 제3권1호
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• pp.52-60
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• 1997

In genetic algorithms(GA), a crossover is performed only at one or two places of a chromosome, and the fixed probabilities of crossover and mutation have been used during the entire generation. A GA with dynamic mutation is known to be superior to GAs with static mutation in performance, but so far no efficient dynamic mutation method has been presented. Accordingly in this paper, a GA is proposed to perform a uniform crossover based on the nucleotide(NU) concept, where DNA and RNA consist of NUs and also a concrete way to vary the probabilities of crossover and mutation dynamically for every generation is proposed. The efficacy of the proposed GA is demonstrated by its application to the unimodal, multimodal and nonlinear control problems, respectively. Simulation results show that in the convergence speed to the optimal value, the proposed GA was superior to existing ones, and the performance of GAs with varying probabilities of the crossover and the mutation improved as compared to GAs with fixed probabilities of the crossover and mutation. And it also shows that the NUs function as the building blocks and so the improvement of the proposed algorithm is supported by the building block hypothesis.

• 한국지능시스템학회논문지
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• 제13권1호
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• pp.37-44
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• 2003

본 논문은 DNA coding 방법과 Genetic Algorithm(GA)을 사용하여 numeric(0~9) 패턴인식 성능을 비교 평가하였다. 이진스트링의 개체 집단 위에서 모의진화를 일으켜 효율적으로 최적 해를 탐색하는 GA와, 생체 분자인 DNA를 계산의 도구 및 정보 저장도구로 사용하며, Adenine(A), Cytosine(C), Guanine(G), Thymine(T)등의 4가지 염기를 사용하는 DNA coding 방법을 이용하여 numeric 패턴인식을 수행하였다. DNA coding 방법과 GA의 성능을 비교 평가하기 위해서 selection, crossover, mutation 등의 GA연산자를 DNA coding에 동일하게 적용하였다. 실험결과, DNA coding 방법은 GA보다 효과적으로 패턴인식을 수행하였다. GA에 비해 DNA coding 방법의 장점은 스트링의 길이가 가변적이고 해의 중복성을 가지며, 4가지 염기를 이용하기 때문에 해 표현이 다양함을 가지고 있다.