• Journal of Information Technology Services
• /
• v.9 no.4
• /
• pp.187-206
• /
• 2010

Evolutionary acquisition is an alternative to the grand design acquisition approaches. It has activities to make it possible to develop quickly and respond flexibly to changing customer needs and technological opportunities. The Ministry of Defense adopted an evolutionary strategy to acquire defense information systems. but it does not work well always. We look at problems from aspects of acquisition system and project management. We benchmark successful cases for evolutionary acquisition strategy in the DoD, the pubic and the private sector. We suggest an evolutionary strategy for defense information systems. The evolutionary strategy in this study includes an evolutionary acquisition framework, an evolutionary acquisition process, and an evolutionary acquisition guideline for defense information systems. The evolutionary strategy can help to implement evolutionary acquisition process for defense information system, and the process can increase the success rate of projects.

• Proceedings of the KSME Conference
• /
• 2000.11a
• /
• pp.699-704
• /
• 2000

The present paper deals with a multiobjective optimization method based on the co-evolutionary genetic strategy. The co-evolutionary strategy carries out the multiobjective optimization in such way that it optimizes individual objective function as compared with each generation's value while there are more than two genetic evolutions at the same time. In this study, the designs that are out of the given constraint map compared with other objective function value are excepted by the penalty. The proposed multiobjective genetic algorithms are distinguished from other optimization methods because it seeks for the optimized value through the simultaneous search without the help of the single-objective values which have to be obtained in advance of the multiobjective designs. The proposed strategy easily applied to well-developed genetic algorithms since it doesn't need any further formulation for the multiobjective optimization. The paper describes the co-evolutionary strategy and compares design results on the simple structural optimization problem.

• Korean Management Science Review
• /
• v.23 no.1
• /
• pp.75-85
• /
• 2006

The rectilinear Steiner tree problem (RSTP) is to find a minimum-length rectilinear interconnection of a set of terminals in the plane. It is well known that the solution to this problem will be the minimal spanning tree(MST) on some set Steiner points. The RSTP is known to be NP-complete. The RSTP has received a lot of attention in the literature and heuristic and optimal algorithms have been proposed. A key performance measure of the algorithm for the RSTP is the reduction rate that is achieved by the difference between the objective value of the RSTP and that of the MST without Steiner points. A hybrid evolutionary strategy on RSTP based upon nodes set is presented. The computational results show that the hybrid evolutionary strategy is better than the previously proposed other heuristic. The average reduction rate of solutions from the evolutionary strategy is about 11.14%, which is almost similar to that of optimal solutions.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.6 no.12
• /
• pp.3133-3151
• /
• 2012

The optimal price strategy selection of two bounded rational cognitive mobile virtual network operators (MVNOs) in a duopoly spectrum sharing market is investigated. The bounded rational operators dynamically compete to sell the leased spectrum to secondary users in order to maximize their profits. Meanwhile, the secondary users' heterogeneous preferences to rate and price are taken into consideration. The evolutionary game theory (EGT) is employed to model the dynamic price strategy selection of the MVNOs taking into account the response of the secondary users. The behavior dynamics and the evolutionary stable strategy (ESS) of the operators are derived via replicated dynamics. Furthermore, a reward and punishment mechanism is developed to optimize the performance of the operators. Numerical results show that the proposed evolutionary algorithm is convergent to the ESS, and the incentive mechanism increases the profits of the operators. It may provide some insight about the optimal price strategy selection for MVNOs in the next generation cognitive wireless networks.

• Journal of The Korean Association For Science Education
• /
• v.31 no.3
• /
• pp.440-454
• /
• 2011

This study proposes a new instructional strategy and corresponding materials designed from various alternative frameworks to help students understand evolution as a biologically acceptable theory. Biology teachers have normally taught the evolutionary mechanism by means of comparing Lamarckism with natural selection. In this study, a new instructional strategy in which the Lamarckian explanation is first excluded because Lamarckism is known to be subsumed in a learner's cognitive structure as a strong preconception of evolution is suggested for teaching evolution. After mutation theory is introduced, Darwinism including natural selection is explained separately during the next class hour. Corresponding instructional materials that aid student understanding of the evolutionary mechanism were developed using recently published articles on human genetic traits as scientific evolutionary evidence instead of the traditional evolutionary subject matter, giraffe neck. Evolutionary evidence from human genetic traits allows students to exclude anthropocentric thoughts effectively and raise concern for the phenomenon of evolution positively. The administered instructional strategy and materials in this research improved student conception, concern, and belief of evolution and it is believed that they helped students understand the evolutionary mechanism effectively.

• Proceedings of the KSME Conference
• /
• 2001.06b
• /
• pp.724-729
• /
• 2001

This paper discusses the composition of the theory of reinforcement learning, which is applied in real-time learning, and evolutionary strategy, which proves its the superiority in the finding of the optimal solution at the off-line learning method. The individuals are reduced in order to learn the evolutionary strategy in real-time, and new method that guarantee the convergence of evolutionary mutations are proposed. It possible to control the control object varied as time changes. As the state value of the control object is generated, applied evolutionary strategy each sampling time because the learning process of an estimation, selection, mutation in real-time. These algorithms can be applied, the people who do not have knowledge about the technical tuning of dynamic systems could design the controller or problems in which the characteristics of the system dynamics are slightly varied as time changes. In the future, studies are needed on the proof of the theory through experiments and the characteristic considerations of the robustness against the outside disturbances.

• Journal of the Korean Society of Systems Engineering
• /
• v.3 no.1
• /
• pp.9-15
• /
• 2007

An EA(Evolutionary Acquisition) strategy is based on the systems engineering. It is a preferred approach to provide operationally useful capabilities to the warfighter much more quickly than single-step to full capability strategy. Recently, DoD is trying to apply the acquisition process based on the systems engineering. In spite of these trends, efforts of domestic defense acquisition society to this strategy are insufficient. Although an EA strategy has many benefits, there are many constraints to apply it. This study analyzes these constraints and presents applications of the EA strategy to defense R&D programs.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.26 no.6
• /
• pp.1092-1098
• /
• 2002

This paper discusses the composition of the theory of reinforcement teaming, which is applied in real-time teaming, and evolutionary strategy, which proves its the superiority in the finding of the optimal solution at the off-line teaming method. The individuals are reduced in order to team the evolutionary strategy in real-time, and new method that guarantee the convergence of evolutionary mutations are proposed. It is possible to control the control object varied as time changes. As the state value of the control object is generated, applied evolutionary strategy each sampling time because of the teaming process of an estimation, selection, mutation in real-time. These algorithms can be applied, the people who do not have knowledge about the technical tuning of dynamic systems could design the controller or problems in which the characteristics of the system dynamics are slightly varied as time changes. In the future, studies are needed on the proof of the theory through experiments and the characteristic considerations of the robustness against the outside disturbances.

• The Transactions of the Korean Institute of Electrical Engineers P
• /
• v.54 no.2
• /
• pp.61-66
• /
• 2005

This paper presents an unified method which solves real and reactive power dispatch problems for the economic operation of power systems using evolutionary computation such as genetic algorithms(GA), evolutionary programming(EP), and evolution strategy(ES). Many conventional methods to this problem have been proposed in the past, but most of these approaches have the common defect of being caught to a local minimum solution. The proposed methods, applied to the IEEE 30-bus system, were run for 10 other exogenous parameters and composed of P-optimization module and Q-optimization module. Each simulation result, by which evolutionary computations are compared and analyzed, shows the possibility of applications of evolutionary computation to large scale power systems.

• Proceedings of the KSME Conference
• /
• 2001.06b
• /
• pp.105-109
• /
• 2001

This paper discuss the real-time self-tuning learning control based on evolutionary computation, which proves its the superiority in the finding of the optimal solution at the off-line learning method. The individuals are reduced in order to learn the evolutionary strategy in real-time, and new method that guarantee the convergence of evolutionary mutations are proposed. It possible to control the control object varied as time changes. As the state value of the control object is generated, applied evolutionary strategy each sampling time because the learning process of an estimation, selection, mutation in real-time. These algorithms can be applied, the people who do not have knowledge about the technical tuning of dynamic systems could design the controller or problems in which the characteristics of the system dynamics are slightly varied as time changes.