• 제어로봇시스템학회논문지
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• 제4권2호
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• pp.256-263
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• 1998

Among process synthesis problems, the heat exchanger network (HEN) has been subjected to the most concentrated effort because this kind of problems was well defined for solving it and 20-30% energy savings could be realized in the present chemical processes. In this paper, we use an evolutionary approach for HEN synthesis because this approach can overcome the local optimum and combine some heuristic rules. The basic evolutionary approach is composed of three parts, that is, initialization step, growth step and mutation step, as in the simulated annealing and genetic algorithm. This algorithm uses the ecological rule that a better cell will live and worse cell should decompose after repeated generations. With this basic concept, a new procedure is developed and a more efficient method is proposed to generate initial solutions. Its effectiveness is shown using test examples.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.93.1-93
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• 2001

An evolutionary approach for the design of Fuzzy Logic Systems(FLSs) is proposed. Membership functions(MFs) in Takagi-Sugeno type fuzzy logic system is optimized through evolutionary process. Output singleton values are obtained through pseudo-inverse method. The proposed technique is unique for that, to prevent overfilling phenomenon, limited-level RBF membership functions are used and the new fitness function is invented. To show the effectiveness of the proposed method, some simulations results on model identification are given.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.531-531
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• 2000

The challenge is to control unstable nonlinear dynamic systems using only sparse feedback from the environment concerning its performance. The design of such controllers can be achieved by evolving neural networks. An evolutionary approach to train neural networks in realtime is proposed. Evolutionary strategies adapt the weights of neural networks and the threshold values of neuron's synapses. The proposed method has been successfully implemented for pole balancing problem.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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• pp.195-204
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• 2003

Network optimization is being increasingly important and fundamental issue in the fields such as engineering, computer science, operations research, transportation, telecommunication, decision support systems, manufacturing, and airline scheduling. Networks provide a useful way to modeling real world problems and are extensively used in practice. Many real world applications impose on more complex issues, such as, complex structure, complex constraints, and multiple objects to be handled simultaneously and make the problem intractable to the traditional approaches. Recent advances in evolutionary computation have made it possible to solve such practical network optimization problems. The invited talk introduces a thorough treatment of evolutionary approaches, i.e., hybrid genetic algorithms approach to network optimization problems, such as, fixed charge transportation problem, minimum cost and maximum flow problem, minimum spanning tree problem, multiple project scheduling problems, scheduling problem in FMS.

• Transactions on Control, Automation and Systems Engineering
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• 제1권2호
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• pp.147-152
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• 1999

It is an interesting area in the field of artifical intelligence to find an analytic model of cooperative structure for multiagent system accomplishing a given task. Usually it is difficult to design controllers for multi-agent systems without a comprehensive knowledge about the system. One of the way to overcome this limitation is to implement an evolutionary approach to design the controllers. This paper introduces the use of a genetic algorithm to discover a fuzzy logic controller with rules that govern emergent agents solving a pursuit problem in a continuous world. Simulation results indicate that, given the complexity of the problem, an evolutionary approach to find the fuzzy logic controller seems to be promising.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2003년도 춘계학술대회
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• pp.1124-1129
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• 2003

In mechanical design, design process is mainly composed of design, explanation and evaluation. In this paper, Using Genetic Algorithms (GA), Evolutionary computation is introduced as new design process. This method promote the efficiency and power of design. Due to the known characteristics of the stage, the approach basically involves a synthetic design method with the composition of building blocks representing the elements of mechanical objects. In order for the building blocks to be more suitable for representation and evolution of mechanical structures, Elementary Cell Blocks (ECBs) are introduced as new building blocks. In this paper, we have demonstrated the implementation of the approach with the design of gear systems.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회/대한산업공학회 2003년도 춘계공동학술대회
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• pp.1071-1076
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• 2003

Evolutionary algorithm is recognized as a promising approach to solving multi-objective combinatorial optimization problems. When no preference information of decision makers is given, multi-objective optimization problems have been commonly used to search for diverse and good Pareto optimal solution. In this paper we propose a new multi-objective evolutionary algorithm based on competitive coevolutionary algorithm, and demonstrate the applicability of the algorithm. The proposed algorithm is designed to promote both population diversity and rapidity of convergence. To achieve this, the strategies of fitness evaluation and the operation of the Pareto set are developed. The algorithm is applied to job shop scheduling problems (JSPs). The JSPs have two objectives: minimizing makespan and minimizing earliness or tardiness. The proposed algorithm is compared with existing evolutionary algorithms in terms of solution quality and diversity. The experimental results reveal the effectiveness of our approach.

• 대한기계학회논문집A
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• 제29권7호
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• pp.1013-1021
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• 2005

In this paper, we developed an automatic design optimization system for parametric shape optimization of cooling passages inside axial turbine blades. A parallel three-dimensional thermoelasticity finite element analysis code from an open source system was used to perform automatic thermal and stress analysis of different blade configuration. The developed code was connected to an evolutionary optimizer and built in a design optimization system. Using the optimization system, 279 feasible and optimal solutions were searched. It is provided not only one best solution of the searched solutions, but also information of variation structure and correlation of the 279 solutions in function, variable, and real design spaces. To explore design information, it is proposed a new interpretation approach based on evolutionary clustering and principal component analysis. The interpretation approach might be applicable to the increasing demands in the general area of design optimization.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1998년도 제13차 학술회의논문집
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• pp.507-512
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• 1998

It is an interesting area in the field of artificial intelligence to and an analytic model of cooperative structure for multi-agent system accomplishing a given task. Usually it is difficult to design controllers for multi-agent systems without a comprehensive knowledge about the system. One of the way to overcome this limitation is to implement an evolutionary approach to design the controllers. This paper introduces the use of a genetic algorithm to discover a fuzzy logic controller with rules that govern emergent co-operative behavior: A modified genetic algorithm was applied to automating the discovery of a fuzzy logic controller jot multi-agents playing a pursuit game. Simulation results indicate that, given the complexity of the problem, an evolutionary approach to and the fuzzy logic controller seems to be promising.

• 아동학회지
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• 제26권5호
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• pp.185-204
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• 2005

This paper demonstrated how application of evolutionary knowledge to developmental perspectives enhances understanding of human ontogeny. Evolutionary Developmental Psychology (EDP) explains human behavior through evolutionary principles and focuses on ontogeny rather than phylogeny. In this paper, the authors review concepts of evolution, adaptations, and the processes of evolution from EDP perspectives. The definition and basic assumptions of EDP are introduced, followed by explanations of how evolution happens in ontogeny by looking at developmental systems approaches, concepts of ontogenetic and deferred adaptations, evolution of childhood, and brain plasticity. Possible pathways of evolution in ontogeny are also discussed. Finally, some research methodology for applying EDP to child development is suggested with specific hypotheses and studies.