• 한국경영과학회지
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• 제29권2호
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• pp.77-95
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• 2004

This paper deals with the process planning of flexible manufacturing systems (FMS) with various flexibilities and multiple objectives. The consideration of the manufacturing flexibility is crucial for the efficient utilization of FMS. The machine, tool, sequence, and process flexibilities are considered In this research. The flexibilities cause to increase the Problem complexity. To solve the process planning problem, an this paper an evolutionary algorithm is used as a methodology. The algorithm is named multiobjective competitive evolutionary algorithm (MOCEA), which is developed in this research. The feature of MOCEA is the incorporation of competitive coevolution in the existing multiobjective evolutionary algorithm. In MOCEA competitive coevolution plays a role to encourage population diversity. This results in the improvement of solution quality and, that is, leads to find diverse and good solutions. Good solutions means near or true Pareto optimal solutions. To verify the Performance of MOCEA, the extensive experiments are performed with various test-bed problems that have distinct levels of variations in the four kinds of flexibilities. The experiments reveal that MOCEA is a promising approach to the multiobjective process planning of FMS.

• 대한산업공학회지
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• 제27권3호
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• pp.239-249
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• 2001

Competitive coevolution models, often called host-parasite models, are searching models that imitate the biological coevolution that is a series of reciprocal changes in two competing species. The models are known to be an effective method of solving complex and dynamic problems such as game problems, neural network design problems and constraint satisfaction problems. However, previous models consider only ectoparasites that live on the outside of the host when designing the models, not considering endoparasites that live on the inside of the host. This has a limitation to exploiting some information. In this paper, we develop an artificial adaptation model simulating the process in which hosts coevolve with both ectoparasites and endoparasites. In the model, the endoparasites play important roles as follows. By means of them, we can keep the history on results of previous competition between hosts and parasites, and use endogeneous fitness, not exogeneous. Extensive experiments are carried out to show the coevolution phenomenon and to verify the performance of the proposed model. Nim game problems and neural network problems are used as test-bed problems. The results are reported in this paper.

• 대한산업공학회지
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• 제28권1호
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• pp.87-98
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• 2002

A competitive coevolutionary algorithm is a probabilistic search method that imitates coevolution process through evolutionary arms race. The algorithm has been used to solve adversarial problems. In the algorithms, the selection of competitors is needed to evaluate the fitness of an individual. The goal of this study is to compare and analyze several competition strategies in terms of solution quality, convergence speed, balance between competitive coevolving species, population diversity, etc. With two types of test-bed problems, game problems and solution-test problems, extensive experiments are carried out. In the game problems, sampling strategies based on fitness have a risk of providing bad solutions due to evolutionary unbalance between species. On the other hand, in the solution-test problems, evolutionary unbalance does not appear in any strategies and the strategies using information about competition results are efficient in solution quality. The experimental results indicate that the tournament competition can progress an evolutionary arms race and then is successful from the viewpoint of evolutionary computation.

• 대한산업공학회지
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• 제26권2호
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• pp.101-109
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• 2000

A competitive coevolutionary algorithm is a probabilistic search method that imitates the biological process that two or more species competitively coevolve through evolutionary arms race. The algorithm has been used to efficiently solve adversarial problems that can be formulated as the search for a solution that is correct over a large space of test cases. We develop an efficient competitive coevolutionary algorithm to solve adversarial problems with high complexity. The algorithm developed in this paper employs three methods: tournament competitions, exchanging of entry fee, and localized coevolution. Analyzed in this paper are the effects of the methods on the performance of the proposed algorithm. The extensive experiments show that our algorithm can progress an evolutionary arms race between competitive coevolving species and then outperforms existing approaches to solving the adversarial problems.

• 한국심리학회지 : 문화 및 사회문제
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• 제18권1호
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• pp.97-110
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• 2012

19세기부터 현재까지 산업화된 국가들을 중심으로 출산율이 급락하고 있는 전세계적인 현상은 진화적인 관점에서 선뜻 이해하기 어렵다. 왜 자원이 더 풍부해진 현대에 들어서 사람들은 자식수를 자발적으로 줄이는가? 본 논문은 현대의 저출산 현상을 설명하는 다양한 진화적 접근들을 요약하고, 이를 토대로 우리 사회의 저출산 문제를 해결할 실마리를 얻고자 한다. 1) 현대의 극히 낮은 출산율은 수렵-채집 생활에 맞추어진 우리의 심리적 적응이 진화적으로 낯선 환경과 불협화음을 일으킴에 따른 부적응적인 부산물이라는 가설, 2) 사회적으로 성공한 사람들이 자녀를 적게 낳는 행동이 전파되거나, 가족 중심의 네트워크가 붕괴하여 출산의 중요성이 덜 강조됨에 따라 저출산이 야기되었다는 유전자-문화 공진화 가설, 그리고 3) 부모가 자녀에게 투자하는 양이 대단히 많이 요구되는 현대의 환경에서 극심한 저출산은 부모의 장기적인 적합도를 최대화하는 적응적인 형질이라는 가설을 차례대로 검토한다. 저출산에 대한 진화적 관점은 저소득층의 출산을 지원하는 정책보다는 모든 사회경제적 계층에서 자녀를 장차 경쟁력 있는 성인으로 키우기 위한 비용을 줄이는 정책을 추진하는 것이 더 효과적임을 시사한다.