• Title, Summary, Keyword: Co-evolutionary algorithm

Search Result 49, Processing Time 0.031 seconds

Design of Fuzzy Controller Using Parasitic Co-evolutionary Algorithm (기생적 공진화 알고리즘을 이용한 퍼지 제어기 설계)

  • 심귀보;변광섭
    • Journal of Institute of Control, Robotics and Systems
    • /
    • v.10 no.11
    • /
    • pp.1071-1076
    • /
    • 2004
  • It is a fuzzy controller that it is the most used method in the control of non-linear system. The most important part in the fuzzy controller is a design of fuzzy rules. Many algorithm that design fuzzy rules have proposed. And attention to the evolutionary computation is increasing in the recent days. Among them, the co-evolutionary algorithm is used in the design of optimal fuzzy rule. This paper takes advantage of a schema co-evolutionary algorithm. In order to verify the efficiency of the schema co-evolutionary algorithm, a fuzzy controller for the mobile robot control is designed by the schema co-evolutionary algorithm and it is compared with other parasitic co-evolutionary algorithm such as a virus-evolutionary genetic algorithm and a co-evolutionary method of Handa.

Optimal Design of the 2-Layer Fuzzy Controller using the Schema Co-Evolutionary Algorithm (Schema Co-Evolutionary Algorithm을 이용한 2-Layer Fuzzy Controller의 최적 설계)

  • Sim, Kwee-Bo;Byun, Kwang-Sub
    • Journal of Korean Institute of Intelligent Systems
    • /
    • v.14 no.2
    • /
    • pp.228-233
    • /
    • 2004
  • Nowadays, the robot with various and complex functions is required. previous algorithms, however, cannot satisfy the requirement. In order to solve these problems, we introduce the 2-Layer Fuzzy Controller, which has a small number of fuzzy rules corresponding to various inputs and outputs. Also, it controls robustly and effectively an object. The main problem in the fuzzy controller is how to design the fuzzy rule. This paper designs the optimal 2-layer fuzzy controller using the Schema Co-Evolutionary Algorithm. The schema co-evolutionary algorithm can find more rapidly and excellently than simple genetic algorithm does.

Co-Evolutionary Algorithm and Extended Schema Theorem

  • Sim, Kwee-Bo;Jun, Hyo-Byung
    • Journal of the Korean Society for Industrial and Applied Mathematics
    • /
    • v.2 no.1
    • /
    • pp.95-110
    • /
    • 1998
  • Evolutionary Algorithms (EAs) are population-based optimization methods based on the principle of Darwinian natural selection. The representative methodology in EAs is genetic algorithm (GA) proposed by J. H. Holland, and the theoretical foundations of GA are the Schema Theorem and the Building Block Hypothesis. In the meaning of these foundational concepts, simple genetic algorithm (SGA) allocate more trials to the schemata whose average fitness remains above average. Although SGA does well in many applications as an optimization method, still it does not guarantee the convergence of a global optimum in GA-hard problems and deceptive problems. Therefore as an alternative scheme, there is a growing interest in a co-evolutionary system, where two populations constantly interact and co-evolve in contrast with traditional single population evolutionary algorithm. In this paper we show why the co-evolutionary algorithm works better than SGA in terms of an extended schema theorem. And predator-prey co-evolution and symbiotic co-evolution, typical approaching methods to co-evolution, are reviewed, and dynamic fitness landscape associated with co-evolution is explained. And the experimental results show a co-evolutionary algorithm works well in optimization problems even though in deceptive functions.

  • PDF

Optimal Design of a 2-Layer Fuzzy Controller Using the Schema Co-Evolutionary Algorithm

  • Byun, Kwang-Sub;Sim, Kwee-Bo
    • International Journal of Fuzzy Logic and Intelligent Systems
    • /
    • v.4 no.3
    • /
    • pp.341-346
    • /
    • 2004
  • Nowadays, versatile robots are developed around the world. Novel algorithms are needed for controlling such robots. A 2-Layer fuzzy controller can deal with many inputs as well as many outputs, and its overall structure is much simpler than that of a general fuzzy controller. The main problem encountered in fuzzy control is the design of the fuzzy controller. In this paper, the fuzzy controller is designed by the schema co-evolutionary algorithm. This algorithm can quickly and easily find a global solution. Therefore, the schema co-evolutionary algorithm is used to design a 2-layer fuzzy controller in this study. We apply it to a mobile robot and verify the efficacy of the 2-layer fuzzy controller and the schema co-evolutionary algorithm through the experiments.

Optimal Design of a 2-Layer Fuzzy Controller using the Schema Co-Evolutionary Algorithm

  • Park Chang-Hyun;Sim Kwee-Bo
    • International Journal of Control, Automation, and Systems
    • /
    • v.3 no.3
    • /
    • pp.403-410
    • /
    • 2005
  • Nowadays, versatile robots are developed around the world. Novel algorithms are needed for controlling such robots. A 2-Layer fuzzy controller can deal with many inputs as well as many outputs, and its overall structure is much simpler than that of a general fuzzy controller. The main problem encountered in fuzzy control is the design of the fuzzy controller. In this paper, the fuzzy controller is designed by the schema co-evolutionary algorithm. This algorithm can quickly and easily find a global solution. Therefore, the schema co-evolutionary algorithm is used to design a 2-layer fuzzy controller in this study. We apply it to a mobile robot and verify the efficacy of the 2-layer fuzzy controller and the schema co-evolutionary algorithm through the experiments.

Game Model Based Co-evolutionary Solution for Multiobjective Optimization Problems

  • Sim, Kwee-Bo;Kim, Ji-Yoon;Lee, Dong-Wook
    • International Journal of Control, Automation, and Systems
    • /
    • v.2 no.2
    • /
    • pp.247-255
    • /
    • 2004
  • The majority of real-world problems encountered by engineers involve simultaneous optimization of competing objectives. In this case instead of single optima, there is a set of alternative trade-offs, generally known as Pareto-optimal solutions. The use of evolutionary algorithms Pareto GA, which was first introduced by Goldberg in 1989, has now become a sort of standard in solving Multiobjective Optimization Problems (MOPs). Though this approach was further developed leading to numerous applications, these applications are based on Pareto ranking and employ the use of the fitness sharing function to maintain diversity. Another scheme for solving MOPs has been presented by J. Nash to solve MOPs originated from Game Theory and Economics. Sefrioui introduced the Nash Genetic Algorithm in 1998. This approach combines genetic algorithms with Nash's idea. Another central achievement of Game Theory is the introduction of an Evolutionary Stable Strategy, introduced by Maynard Smith in 1982. In this paper, we will try to find ESS as a solution of MOPs using our game model based co-evolutionary algorithm. First, we will investigate the validity of our co-evolutionary approach to solve MOPs. That is, we will demonstrate how the evolutionary game can be embodied using co-evolutionary algorithms and also confirm whether it can reach the optimal equilibrium point of a MOP. Second, we will evaluate the effectiveness of our approach, comparing it with other methods through rigorous experiments on several MOPs.

Accelerated Co-evolutionary Algorithms

  • Kim, Jong-Han;Tahk, Min-Jea
    • International Journal of Aeronautical and Space Sciences
    • /
    • v.3 no.1
    • /
    • pp.50-60
    • /
    • 2002
  • A new co-evolutionary algorithm, of which the convergence speed is accelerated by neural networks, is proposed and verified in this paper. To reduce computational load required for co-evolutionary optimization processes, the cost function and constraint information is stored in the neural networks, and the extra offspring group, whose cost is computed by the neural networks, is generated. It increases the offspring population size without overloading computational effort; therefore, the convergence speed is accelerated. The proposed algorithm is applied to attitude control design of flexible satellites, and it is verified by computer simulations and experiments using a torque-free air bearing system.

Game Theory Based Co-Evolutionary Algorithm (GCEA) (게임 이론에 기반한 공진화 알고리즘)

  • Sim, Kwee-Bo;Kim, Ji-Youn;Lee, Dong-Wook
    • Journal of Korean Institute of Intelligent Systems
    • /
    • v.14 no.3
    • /
    • pp.253-261
    • /
    • 2004
  • Game theory is mathematical analysis developed to study involved in making decisions. In 1928, Von Neumann proved that every two-person, zero-sum game with finitely many pure strategies for each player is deterministic. As well, 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. Not the rationality but through the process of Darwinian selection, a population of agents can evolve to an Evolutionary Stable Strategy (ESS) introduced by Maynard Smith. Keeping pace with these game theoretical studies, the first computer simulation of co-evolution was tried out by Hillis in 1991. Moreover, Kauffman proposed NK model to analyze co-evolutionary dynamics between different species. He showed how co-evolutionary phenomenon reaches static states and that these states are Nash equilibrium or ESS introduced in game theory. Since the studies about co-evolutionary phenomenon were started, however many other researchers have developed co-evolutionary algorithms, in this paper we propose Game theory based Co-Evolutionary Algorithm (GCEA) and confirm that this algorithm can be a solution of evolutionary problems by searching the ESS.To evaluate newly designed GCEA approach, we solve several test Multi-objective Optimization Problems (MOPs). From the results of these evaluations, we confirm that evolutionary game can be embodied by co-evolutionary algorithm and analyze optimization performance of GCEA by comparing experimental results using GCEA with the results using other evolutionary optimization algorithms.

Co-Evolutionary Model for Solving the GA-Hard Problems (GA-Hard 문제를 풀기 위한 공진화 모델)

  • Lee Dong-Wook;Sim Kwee-Bo
    • Journal of Korean Institute of Intelligent Systems
    • /
    • v.15 no.3
    • /
    • pp.375-381
    • /
    • 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.

Study on Diversity of Population in Game model based Co-evolutionary Algorithm for Multiobjective optimization (다목적 함수 최적화를 위한 게임 모델에 기반한 공진화 알고리즘에서의 해집단의 다양성에 관한 연구)

  • Lee, Hea-Jae;Sim, Kwee-Bo
    • Journal of Korean Institute of Intelligent Systems
    • /
    • v.17 no.7
    • /
    • pp.869-874
    • /
    • 2007
  • In searching for solutions to multiobjective optimization problem, we find that there is no single optimal solution but rather a set of solutions known as 'Pareto optimal set'. To find approximation of ideal pareto optimal set, search capability of diverse individuals at population space can determine the performance of evolutionary algorithms. This paper propose the method to maintain population diversify and to find non-dominated alternatives in Game model based Co-Evolutionary Algorithm.