• Journal of information and communication convergence engineering
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• 제19권3호
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• pp.155-160
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• 2021

Differential evolution is an efficient algorithm for solving continuous optimization problems. However, its performance deteriorates rapidly, and the runtime increases exponentially when differential evolution is applied for solving large-scale optimization problems. Hence, a novel cooperative coevolution differential evolution based on Spark (known as SparkDECC) is proposed. The divide-and-conquer strategy is used in SparkDECC. First, the large-scale problem is decomposed into several low-dimensional subproblems using the random grouping strategy. Subsequently, each subproblem can be addressed in a parallel manner by exploiting the parallel computation capability of the resilient distributed datasets model in Spark. Finally, the optimal solution of the entire problem is obtained using the cooperation mechanism. The experimental results on 13 high-benchmark functions show that the new algorithm performs well in terms of speedup and scalability. The effectiveness and applicability of the proposed algorithm are verified.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2022년도 춘계학술대회
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• pp.665-666
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• 2022

미분 진화는 연속 최적화 문제에 대한 효율적인 알고리즘이다. 그러나 대규모 최적화 문제를 해결하기 위해 미분 진화를 적용하면 성능이 빠르게 저하되고 런타임이 기하급수적으로 증가한다. 이 문제를 극복하기 위해 Spark(SparkDECC라고 함)를 기반으로 하는 새로운 협력 공진화 미분 진화를 제안한다. 분할 정복 전략은 SparkDECC에서 사용된다.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2021년도 추계학술대회
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• pp.559-560
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• 2021

차등 진화는 연속 최적화 문제를 해결하기 위한 효율적인 알고리즘이다. 그러나 대규모 최적화 문제를 해결하기 위해 차등 진화를 적용하면 성능이 급격히 저하되고 런타임이 기하급수적으로 증가한다. 따라서 Spark(SparkDECC로 알려짐)를 기반으로 하는 새로운 협력 공진화 차동 진화가 제안된다. 분할 정복 전략은 SparkDECC에서 사용된다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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• pp.690-693
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• 2003

The purpose of two player game is that a player beats an enemy. In order to win to various enemies, a learning of various strategies is indispensable. However, the optimal action to overcome the enemies will change when the game done over and again because the enemy's actions also change dynamically. Sol it is din-cult that the player aquires the optimal action and that the specific player keeps winning to various enemies. Species who have a competition relation and affect other's existence is called a coevolution. Coevolution has recently attracred considerable interest in the community of Artificial Life and Evolutionary Computation(1). In this paper, we apply Classifier System for agent team to two player game. A reward and a penalty are given to the used rules when the agent achieve specific action in the game and each team's rulebase are evaluated based on the ranking in the league. We show that all teams can acquire the optimal actions by coevolution.

• 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.

• 한국산학기술학회논문지
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• 제21권12호
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• pp.268-277
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• 2020

대학이 직면하고 있는 생존환경을 고려할 때, 대학의 생존전략은 생태계적 사고에 기반 하는 공진화 전략구축이 되어야 함을 이 연구는 강조한다. 따라서 대학은 지역혁신생태계의 핵심역할을 수행하기 위한 전략구축이 요청되는 바, 대학혁신생태계를 네 단계로 구분하고, 각 단계별 공진화전략을 구축하는 이론적 틀을 제시하는 것을 연구 목적으로 한다. 따라서 연구방법은 이론적 문헌연구에 초점을 두었으며, 대학혁신생태계 논리구축의 이론적 프레임워크는 Moore의 기업생태계 연구 모형(1996)을 원용 하였다. 대학의 생태계혁신전략은 네 개의 발전단계로 구분하고, 단계별 공진화 전략을 제시한다. 개척단계에서 대학의 공진화전략은 대학주도 혁신생태계의 가치창조를, 확장단계에서는 임계치 확보, 권위단계에서는 권위와 교섭력 지속, 마지막 쇄신단계에서는 성과의 지속적 개선을 제시하였다. 특히 대학의 혁신생태계 구축 및 확산의 가능성은 지역산업과 정부정책과의 연동성과 적실성을 강조하였다. 이러한 대학-지역 혁신생태계 모형은 정부 재정지원사업의 효과성을 제고할 수 있는 이론적 근거를 제시하였다는 측면에서, 그리고 개별 대학에게는 자신의 생태계구축 단계에 적합한 공진화 전략을 위한 틀을 제공한다는 측면에서 연구의 의의가 있다.

• 한국경영과학회지
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• 제37권3호
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• pp.39-55
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• 2012

This paper presents an endosymbiotic evolutionary algorithm (EEA) to solve both problems of line balancing and model sequencing in a mixed-model two-sided assembly line (MMtAL) simultaneously. It is important to have a proper balancing and model sequencing for an efficient operation of MMtAL. EEA imitates the natural evolution process of endosymbionts, which is an extension of existing symbiotic evolutionary algorithms. It provides a proper balance between parallel search with the separated individuals representing partial solutions and integrated search with endosymbionts representing entire solutions. The strategy of localized coevolution and the concept of steady-state genetic algorithms are used to improve the search efficiency. The experimental results reveal that EEA is better than two compared symbiotic evolutionary algorithms as well as a traditional genetic algorithm in solution quality.

• International Journal of Control, Automation, and Systems
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• 제2권4호
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• pp.463-474
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• 2004

Game theory is a method of mathematical analysis developed to study the decision making process. In 1928, Von Neumann mathematically proved that every two-person, zero-sum game with many pure finite strategies for each player is deterministic. In the early 50's, Nash presented another concept as the basis for a generalization of Von Neumann's theorem. Another central achievement of game theory is the introduction of evolutionary game theory, by which agents can play optimal strategies in the absence of rationality. Through the process of Darwinian selection, a population of agents can evolve to an Evolutionary Stable Strategy (ESS) as introduced by Maynard Smith in 1982. Keeping pace with these game theoretical studies, the first computer simulation of coevolution was tried out by Hillis. Moreover, Kauffman proposed the NK model to analyze coevolutionary dynamics between different species. He showed how coevolutionary phenomenon reaches static states and that these states are either Nash equilibrium or ESS in game theory. Since studies concerning coevolutionary phenomenon were initiated, there have been numerous other researchers who have developed coevolutionary algorithms. In this paper we propose a new coevolutionary algorithm named Game theory based Coevolutionary Algorithm (GCEA) and we confirm that this algorithm can be a solution of evolutionary problems by searching the ESS. To evaluate this newly designed approach, we solve several test Multiobjective Optimization Problems (MOPs). From the results of these evaluations, we confirm that evolutionary game can be embodied by the coevolutionary algorithm and analyze the optimization performance of our algorithm by comparing the performance of our algorithm with that of other evolutionary optimization algorithms.

• Journal of Ecology and Environment
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• 제46권3호
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• pp.172-182
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• 2022

Background: The coronavirus problem is an ecological problem stemming from a sudden change in the relationship between parasites and hosts. Ecologists judge organisms that are established out of their original territory as exotic species. Unlike in their original habitat, these exotic species become very aggressive in their newly settled habitat. Coronavirus infection damage was bigger in Europe or the United States than that in the country of its origin, China, and its neighboring countries. Therefore, coronavirus infection damage resembles the damage due to the invasive species. Results: Exotic species are found in places with similar environmental conditions to those of their origin when introduced to other ecological regions. However, there are few ecological ill effects in their place of origin, while the damage is usually severe in the ecological regions in which it is introduced. According to historical records, exotic infectious diseases, such as European smallpox and measles, also showed a similar trend and caused great damage in newly established places. Therefore, it is expected that measures to manage exotic species could be used for the prevention of exotic infectious diseases such as the coronavirus. Conclusions: Prevention comes first in the management of exotic species, and in order to come up with preventive measures, it is important to collect information on the characteristics of related organisms and their preferred environment. In this respect, ecosystem management measures such as exotic species management measures could be used as a reference to prevent and suppress the spread. To put these measures into practice, it is urgently required to establish an international integrated information network for collecting and exchanging information between regions and countries. Furthermore, a systematic ecosystem-management strategy in which natural and human environments could continue sustainable lives in their respective locations may serve as a countermeasure to prevent infectious diseases.

• 디지털융복합연구
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• 제14권8호
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• pp.293-303
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• 2016

근래에 진화 연산을 활용한 교섭 게임의 분석은 게임 이론 분야에서 중요한 문제로 다루어지고 있다. 본 논문은 교섭 게임에서 진화 연산을 사용하여 이기종 인공 에이전트 간의 상호 작용 및 공진화 과정을 조사하였다. 교섭게임에 참여하는 진화전략 에이전트들로서 유전자 알고리즘(GA), 입자군집최적화(PSO) 및 차분진화알고리즘(DE) 3종류를 사용하였다. GA-agent, PSO-agent 및 DE-agent의 3가지 인공 에이전트들 간의 공진화 실험을 통해 교섭게임에서 가장 성능이 우수한 진화 계산 에이전트가 무엇인지 관찰 실험하였다. 시뮬레이션 실험결과, PSO-agent가 가장 성능이 우수하고 그 다음이 GA-agent이며 DE-agent가 가장 성능이 좋지 않다는 것을 확인하였다. PSO-agent가 교섭 게임에서 성능이 가장 우수한 이유를 이해하기 위해서 게임 완료 후 인공 에이전트 전략들을 관찰하였다. PSO-agent는 거래 실패로 인해 보수를 얻지 못하는 것을 감수하고서라도 가급적 많은 보수를 얻기 위한 방향으로 진화하였다는 것을 확인하였으며, 반면에 GA-agent와 DE-agent는 소량의 보수를 얻더라도 거래를 성공시키는 방향으로 진화하였다는 것을 확인하였다.