Naoyuki Kubota

The role of virus infection in virus-evolutionary genetic algorithm

This paper deals with a genetic algorithm based on virus theory of evolution (VEGA). The VEGA realizes horizontal propagation and vertical inheritance of genetic information in a population with virus infection operators and genetic operators. The main operator of the VEGA is the reverse transcription operator, which plays the roles of a cross over and a selection simultaneously. The convergence and genetic diversity of the VEGA depend on the frequency of the virus infection. We apply the VEGA to a travelling salesman problem, a knapsack problem, and function optimization problems, and discuss the effectiveness of the virus infection through the numerical simulation.

Trajectory generation for redundant manipulator using virus evolutionary genetic algorithm

This paper deals with an application of a virus-evolutionary genetic algorithm (VEGA) to hierarchical trajectory planning of a redundant manipulator. The hierarchical trajectory planning is composed of a trajectory generator and position generator. The position generator generates collision-free intermediate positions of the redundant manipulator. The trajectory generator generates a collision-free trajectory based on some intermediate positions sent from the position generator. To generate a collision-free trajectory of the redundant manipulator, the VEGA is applied to the hierarchical trajectory planning only based on forward kinematics. The VEGA realizes horizontal propagation and vertical inheritance of genetic information in a population of candidate solutions. The main operator of the VEGA is a reverse transcription operator, which plays the roles of a crossover and selection simultaneously. In this paper, self-adaptive mutation is applied to the VEGA for local search of trajectory planning to obtain higher performance and the quick solution. Simulation results of the hierarchical trajectory planning show that the VEGA can generate a collision-free trajectory.

Virus-evolutionary genetic algorithm for a self-organizing manufacturing system

Abstract A virus-evolutionary genetic algorithm (VEGA) based on virus theory of evolution is proposed. The VEGA is composed of a host population of candidate solutions and a virus population of substrings of host individuals. Two new operators are introduced: (1) a reverse transcription operator which overwrites a virus string on a hosts string and, (2) a transduction operator generating a new virus from a host string. In this paper, the VEGA is applied to the traveling salesman problem. The VEGA is also applied to a pallet location problem of a press machining line in a self-organizing manufacturing system, in which a process effectively self-organizes according to other processes. Simulation results show the effectiveness of the proposed algorithm and that the virus population possesses effective schemata.

Genetic algorithm with age structure and its application to self-organizing manufacturing system

The genetic algorithm has recently been demonstrated its effectiveness in optimization issues, but it has two major problems: a premature local convergence and a bias by the genetic drift. In order to solve these problems, we propose a new genetic algorithm with an age structure of a continuous generation model. The new genetic algorithm is applied to a self-organizing manufacturing system-a process which self-organizes to other processes in a flexible manufacturing system environment. The effectiveness of the genetic algorithm with age structure is demonstrated through numerical simulations of the reorganization of a press machining line as an example of the self-organizing manufacturing system.<<ETX>>

Trajectory planning of cellular manipulator system using virus-evolutionary genetic algorithm

This paper deals with an application of a genetic algorithm based on a virus theory of evolution (VEGA) to trajectory planning of a cellular manipulator system. A cellular manipulator system is composed of a large number of autonomous parts and tools. The form of the cellular manipulator system is dynamically reconfigured according to its environment and given tasks. In this paper, the VEGA is applied to a trajectory planning problem. The VEGA realizes a horizontal propagation and a vertical inheritance of genetic information in a population. The main operator of the VEGA is a reverse transcription operator, which plays the roles of a crossover and a selection at the same time. Simulation results of trajectory planning shows that the VEGA generates a collision-free trajectory.

GA algorithms in intelligent robots

This chapter presents the role of genetic algorithms in intelligent robots. In general, the motion planning problems in intelligent robots can be fundamentally split into path planning problems, trajectory planning problems, and task planning problems. These planning faculties have many constraints concerning kinematics and dynamics of the robot and therefore it is very difficult to solve these planning problems. This chapter presents the general application of genetic algorithms to these planning tasks. Furthermore, the chapter discusses a trajectory planning problem for redundant manipulators and a motion planning problem for biped locomotion robots.

Evolutionary transition on Virus-Evolutionary Genetic Algorithm

The paper deals with a genetic algorithm (GA) based on the virus theory of evolution (VEGA) and evolutionary transition of a population. VEGA can self adaptively change the searching ratio between local search and global search according to the current state of population of candidate solutions. In addition, various types of evolutionary optimization methods have been proposed and successfully applied to many optimization problems. However, it is difficult to determine the coding method, genetic operators and selection scheme. To analyze the behavior of GAs, Markov chain analysis, deceptive problems and schema analysis have been discussed. We discuss evolutionary transition concerning fitness improvement through numerical simulation of the traveling salesman problem. The simulation results indicate that particular genetic operators give a population different potentialities for generating candidate solutions and that virus infection operators can generate effective schemata and propagate them to a population evolved with any genetic operators.

Trajectory planning of reconfigurable redundant manipulator using virus-evolutionary genetic algorithm

This paper deals with an application of a virus-evolutionary genetic algorithm (VEGA) to trajectory planning of a reconfigurable redundant manipulator. The form of the reconfigurable redundant manipulator is dynamically reconfigured according to its environment and given tasks. In this paper, the VEGA is applied to a trajectory planning based on forward kinematics. The VEGA realizes horizontal propagation and vertical inheritance of genetic information in a population of candidate solutions. The main operator of the VEGA is a reverse transcription operator, which plays the roles of a crossover and a selection simultaneously. Simulation results of trajectory planning show that the VEGA can generate a collision-free trajectory.

Virus-evolutionary genetic algorithm-coevolution of planar grid model

This paper deals with an ecological model on a planar gird of a genetic algorithm based on virus theory of evolution (E-VEGA). In the E-VEGA, each individual is placed on a planar grid and genetic operators are performed between neighborhoods. The E-VEGA can self-adaptively change searching ratio between global and local searches. The main operator of the E-VEGA is a reverse transcription operator, which plays the roles of a crossover and a selection simultaneously. The convergence and generic diversity of the E-VEGA depend on the frequency and localization of the virus infection. In this paper, we apply the E-VEGA to travelling salesman problem and discuss the coevolution of host and virus population through the numerical simulation.

Virus-evolutionary genetic algorithm-ecological model on planar grid

This paper deals with an ecological model on a planar gird of a genetic algorithm based on the virus theory of evolution (VEGA). VEGA assumes horizontal propagation and vertical inheritance of genetic information in a population with virus infection operators and generic operators. The main operator of VEGA is a reverse transcription operator, which plays the roles of crossover and selection simultaneously. The convergence and genetic diversity of the ecological model of VEGA (E-VEGA) depend on the frequency and localization of the virus infection. We apply E-VEGA to the traveling salesman problem and discuss its effectiveness through numerical simulation.