Hitoshi Kanoh

Dynamic route planning for car navigation systems using virus genetic algorithms

This paper describes a practical dynamic route planning method using real road maps in a wide area. The maps include traffic signals, road classes, and the number of lanes. The proposed solution is using a genetic algorithm adopting viral infection. The method is to use viruses as domain specific knowledge. A part of an arterial road is regarded as a virus. A population of viruses is generated in addition to a population of routes. Crossover and infection determine the near-optimal combination of viruses. When traffic congestion frequently changes during driving, an alternative route can be selected using viruses and other routes in the population in a real time. Experiments in dynamic environments using a real road map with 28000 cars show that the proposed method is superior to the Dijkstra algorithm for use in practical car navigation devices.

Knowledge based genetic algorithm for dynamic route selection

Addresses the problem of selecting a route to a given destination on a road map under a dynamic environment. The proposed solution uses a genetic algorithm adopting viral infection. The method is to use the viruses as domain-specific knowledge. A part of an arterial road is regarded as a virus. We generate a population of viruses in addition to a population of routes. Crossover and infection determine the optimal combination of viruses. When traffic congestion changes during driving, an alternative route can be generated using the viruses and other routes in the population in the shortest time. Experiments using actual road maps show that the infection method is effective for this problem.

Hybrid genetic algorithm for dynamic multi-objective route planning with predicted traffic in a real-world road network

Car navigation equipment in practical use has treated a route planning problem as a single-objective problem. In this paper, we formulate the problem as a dynamic multi-objective problem and show how it can be solved using a GA. There are three objective functions to optimize simultaneously in this problem: route length, travel time that changes rapidly with time, and ease of driving. The proposed method gives the Pareto-optimal set by using both the predicted traffic and a hybrid multi-objective GA (GA + Dijkstra algorithm) so that a driver can choose a favorite route after looking at feasible ones. We give the results of experiments comparing the proposed method with the Dijkstra algorithm and the single-objective GA in applications with a real road map and real traffic data in wide-area road network.

Short-term traffic prediction using fuzzy c-means and cellular automata in a wide-area road network

In this paper, we propose a short-term prediction method for forecasting traffic in a time-series manner for up to one hour ahead for all roads in a wide-area road network. The results of our research enable traffic to be simulated for a wide-area road network based on actual traffic data by combining fuzzy clustering and cellular automata. On application to an actual road network with 3,405 links, the proposed technique was found to be superior to the nearest neighborhood method for traffic prediction at times of congestion outbreak and alleviation and heavy congestion.

Solving constraint-satisfaction problems by a genetic algorithm adopting viral infection

Abstract Several approximate algorithms have been reported to solve large constraint-satisfaction problems (CSPs) within a practical time. While those papers discuss techniques to escape from local optima, this paper describes a method that actively performs global searches. The present method improves the rate of search of genetic algorithms by using viral infection instead of mutation. Partial solutions of a CSP are considered to be viruses, and a population of viruses is created, as well as a population of candidate solutions. The search for a solution is conducted by crossover and infection. Infection substitutes the gene of a virus for the locus decided by the virus. Experimental results using randomly generated CSPs prove that the proposed method is faster that usual genetic algorithms at finding a solution when the constraint density of a CSP is low.

Genetic algorithms for constraint satisfaction problems

Several approximate algorithms using hill-climbing techniques and neural networks have been proposed to solve large constraint satisfaction problems (CSPs) in a practical time. In these proposals, many methods of escaping from local optima are discussed, however, there are very few methods actively perform global search. In this paper we propose a hybrid search method that combines the genetic algorithm with the min-conflicts hill-climbing (MCHC). In our method, the individual that has the fewest conflicts in the population is used as the initial value of MCHC to search locally. The detailed experimental simulation is also performed to prove that the proposed method generally gives better efficiency than the random restarting MCHC when CSPs are sparsely-connected.

Evolutionary Design of Rule Changing Cellular Automata

The difficulty of designing cellular automatons’ transition rules to perform a particular problem has severely limited their applications. In this paper we propose a new programming method of cellular computers using genetic algorithms. We consider a pair of rules and the number of rule iterations as a step in the computer program. The present method is meant to reduce the complexity of a given problem by dividing the problem into smaller ones and assigning a distinct rule to each. Experimental results using density classification and synchronization problems prove that our method is more efficient than a conventional one.

Evaluation of GA-based dynamic route guidance for car navigation using cellular automata

This paper describes a practical dynamic route selection method and a method for its evaluation. A classification is established for the information required by drivers in selecting routes. The advantage of the method is that the driver situation is expressed by environment information, destination information and vehicle information. Reports on the evaluation of navigation on real road maps are few. Experiments with the system in a dynamic environment built from a real road map show that the GA-based method is superior to the Dykstra algorithm for use in practical car navigation devices.

An ALife approach to modeling virtual cities

This paper proposes a novel method that enables automatic modeling of virtual cities. The method makes use of L-systems to generate road networks and the genetic algorithm (GA) to generate building layouts. The road networks are composed of two types of roads-linear flow systems which are generated by using the Tree L-system and cellular networks which are generated by using the Map L-system. A generation procedure for road networks and building layouts is described. Some experimental results of generated road networks and virtual cities are also shown.

Solving constraint satisfaction problems by a genetic algorithm adopting viral infection

Several approximate algorithms have been reported to solve large constraint satisfaction problems (CSPs) in a practical time. While these papers discuss techniques to escape from local optima, the present paper describes a method that actively performs global search. The present method is to improve the rate of search of genetic algorithms using viral infection instead of mutation. The partial solutions of a CSP are considered to be viruses and a population of viruses is created as well as a population of candidate solutions. Search for a solution is conducted by crossover infection substitutes the gene of a virus for the locus decided by the virus. Experimental results using randomly generated CSPs prove that the proposed method is faster than a usual genetic algorithm in finding a solution when the constraint density of a CSP is low.