• Smart Structures and Systems
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• v.15 no.3
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• pp.735-749
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• 2015

The major difficulty of using Bayesian probabilistic inference for system identification is to obtain the posterior probability density of parameters conditioned by the measured response. The posterior density of structural parameters indicates how plausible each model is when considering the uncertainty of prediction errors. The Markov chain Monte Carlo (MCMC) method is a widespread medium for posterior inference but its convergence is often slow. The differential evolution adaptive Metropolis-Hasting (DREAM) algorithm boasts a population-based mechanism, which nms multiple different Markov chains simultaneously, and a global optimum exploration ability. This paper proposes an improved differential evolution adaptive Metropolis-Hasting algorithm (IDREAM) strategy to estimate the posterior density of structural parameters. The main benefit of IDREAM is its efficient MCMC simulation through its use of the adaptive Metropolis (AM) method with a mutation strategy for ensuring quick convergence and robust solutions. Its effectiveness was demonstrated in simulations on identifying the structural parameters with limited output data and noise polluted measurements.

• Journal of the Korea Society of Computer and Information
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• v.17 no.4
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• pp.129-137
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• 2012

The queen-bee genetic algorithm that we made by mimicking of the reproduction of queen-bee has considerably improved the performances of genetic algorithm. However, since we used only one queen-bee in the queen-bee genetic algorithm, a problem that individuals of genetic algorithm were driven to one place where the queen-bee existed occurred. This made the performances of the queen-bee genetic algorithm degrade. In order to solve this problem, we introduce a multiple queen-bee evolution method by employing another queen-bee whose fitness is the most significantly increased than its parents as well as the original queen-bee that is the best individual in a generation. This multiple queen-bee evolution makes the probability of falling into local optimum areas decrease and allows the individuals to easily get out of the local optimum areas even if the individuals fall into a local optimum area. This results in increasing the performances of the genetic algorithm. Experimental results with four function optimization problems showed that the performances of the proposed method were better than those of the existing method in the most cases.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.3
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• pp.375-381
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• 2005

Usually genetic algorithms are used to design optimal system. However the performance of the algorithm is determined by the fitness function and the system environment. It is expected that a co-evolutionary algorithm, two populations are constantly interact and co-evolve, is one of the solution to overcome these problems. In this paper we propose three types of co-evolutionary algorithm to solve GA-Hard problem. The first model is a competitive co-evolutionary algorithm that solution and environment are competitively co-evolve. This model can prevent the solution from falling in local optima because the environment are also evolve according to the evolution of the solution. The second algorithm is schema co-evolutionary algorithm that has host population and parasite (schema) population. Schema population supply good schema to host population in this algorithm. The third is game model-based co-evolutionary algorithm that two populations are co-evolve through game. Each algorithm is applied to visual servoing, robot navigation, and multi-objective optimization problem to verify the effectiveness of the proposed algorithms.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.111.3-111
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• 2002

$\textbullet$introduction $\textbullet$CO-evolution Algorithm $\textbullet$Subsumption Architecture $\textbullet$Neural Network $\textbullet$Khepera Robot

• Journal of Korean Association for Spatial Structures
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• v.20 no.1
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• pp.95-102
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• 2020

The structural design of arch roofs or bridges requires the analysis of their unstable behaviors depending on certain parameters defined in the arch shape. Their maintenance should estimate the parameters from observed data. However, since the critical parameters exist in the equilibrium paths of the arch, and a small change in such the parameters causes a significant change in their behaviors. Thus, estimation to find the critical ones should be carried out using a global search algorithm. In this paper we study the parameter estimation for a shallow arch by a quantum-inspired evolution algorithm. A cost functional to estimate the system parameters included in the arch consists of the difference between the observed signal and the estimated signal of the arch system. The design variables are shape, external load and damping constant in the arch system. We provide theoretical and numerical examples for estimation of the parameters from both contaminated data and pure data.

• Korean Institute of Interior Design Journal
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• v.15 no.1 s.54
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• pp.30-38
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• 2006

The new aspect of contemporary architectural design is the computer simulation of morphogenesis and evolution of the organic body. Morphogenesis and evolution is the kind of emergence that is the process of complex pattern formation from simpler rules in complex system. The development comprises the sequence of pattern formation, differentiation, morphogenesis, growth. This study analyzes the application methodology of various biomorphism in contemporary architecture. The methods of generative application by computation in architecture are self-organization, differentiation, growth algorithm via MoSS. And the methods of evolution by computation are genetic algorithm, multi-parameter in environments, phylogenetic cross-over, competing as natural selection, mutation+external constraints, generative algorithm+genetic algorithm via Genr8.

• International Journal of Control, Automation, and Systems
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• v.6 no.2
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• pp.282-287
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• 2008

In distributed mobile robot systems, autonomous robots accomplish complicated tasks through intelligent cooperation with each other. This paper presents behavior learning and online distributed evolution for cooperative behavior of a group of autonomous robots. Learning and evolution capabilities are essential for a group of autonomous robots to adapt to unstructured environments. Behavior learning finds an optimal state-action mapping of a robot for a given operating condition. In behavior learning, a Q-learning algorithm is modified to handle delayed rewards in the distributed robot systems. A group of robots implements cooperative behaviors through communication with other robots. Individual robots improve the state-action mapping through online evolution with the crossover operator based on the Q-values and their update frequencies. A cooperative material search problem demonstrated the effectiveness of the proposed behavior learning and online distributed evolution method for implementing cooperative behavior of a group of autonomous mobile robots.

• Proceedings of the IEEK Conference
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• 2001.06c
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• pp.173-176
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• 2001

This paper proposes training algorithms of neuro-fuzzy systems. First, we introduce a structure training algorithm, which produces the necessary number of hidden nodes from training data. From this algorithm, initial fuzzy rules are also obtained. Second, the parameter training algorithm using evolution strategy is introduced. In order to show their usefulness, we apply our neuro-fuzzy system to a nonlinear system identification problem. It was found from experiments that proposed training algorithms works well.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.11
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• pp.5574-5591
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• 2017

Inspired by the idea of Artificial Immune System, many researches of wireless sensor network (WSN) intrusion detection is based on the artificial intelligent system (AIS). However, a large number of generated detectors, black hole, overlap problem of NSA have impeded further used in WSN. In order to improve the anomaly detection performance for WSN, detector generation mechanism need to be improved. Therefore, in this paper, a Differential Evolution Constraint Multi-objective Optimization Problem based Negative Selection Algorithm (DE-CMOP based NSA) is proposed to optimize the distribution and effectiveness of the detector. By combining the constraint handling and multi-objective optimization technique, the algorithm is able to generate the detector set with maximized coverage of non-self space and minimized overlap among detectors. By employing differential evolution, the algorithm can reduce the black hole effectively. The experiment results show that our proposed scheme provides improved NSA algorithm in-terms, the detectors generated by the DE-CMOP based NSA more uniform with less overlap and minimum black hole, thus effectively improves the intrusion detection performance. At the same time, the new algorithm reduces the number of detectors which reduces the complexity of detection phase. Thus, this makes it suitable for intrusion detection in WSN.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.165.2-165
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• 2001

A number of previous researches in evolutionary algorithm are based on the study of facets we observe in natural evolution. The individuals of species in natural evolution occupy their own niche that is a subdivision of the habitat. This means that two species with the similar requirements cannot live together in the same niche. This is known as the competitive exclusion principle, i.e., complete competitors cannot coexist. In this paper, a new evolutionary programming algorithm adopting this concept is presented. Similarly in the case of natural evolution , the algorithm Includes the concept of niche obtained by partitioning a search space and the competitive exclusion principle performed by migrating individuals. Cell partition and individual migration strategies are used to preserve search diversity as well as to speed up convergence of an ...